Search results for: weather forecast

Authors: Nihit Madireddi, P. A. Mahanwar

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We have aimed to produce a self-cleaning transparent polymer coating with polyurethane (PU) matrix as the latter is highly solvent, chemical and weather resistant having good mechanical properties. Nano-silica modified by 1H, 1H, 2H, 2H-perflurooctyltriethoxysilane was incorporated into the PU matrix for attaining self-cleaning ability through hydrophobicity. The modification was confirmed by particle size analysis and scanning electron microscopy (SEM). Thermo-gravimetric (TGA) studies were carried to ascertain the grafting of silane onto the silica. Several coating formulations were prepared by varying the silica loading content and compared to a commercial equivalent. The effect of dispersion and the morphology of the coated films were assessed by SEM analysis. All coating standardized tests like solvent resistance, adhesion, flexibility, acid, alkali, gloss etc. have been performed as per ASTM standards. Water contact angle studies were conducted to analyze the hydrophobic character of the coating. In addition, the coatings were also subjected to salt spray and accelerated weather testing to analyze the durability of the coating.

Keywords: FAS, nano-silica, PU clear coat, self-cleaning

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Authors: Eyad Almaita

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In this paper, a novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm is used to forecast the hour by hour electrical load demand in Jordan. A small and effective RBFNN model is used to forecast the hourly total load demand based on a small number of features. These features are; the load in the previous day, the load in the same day in the previous week, the temperature in the same hour, the hour number, the day number, and the day type. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminates the need to retrain the RBFNN model again. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data for the period Jan./2012-April/2013 is used train the RBFNN models and the data for the period May/2013- Sep. /2013 is used to validate the models effectiveness.

Keywords: load forecasting, adaptive neural network, radial basis function, short-term, electricity consumption

Procedia PDF Downloads 315

Authors: Arefeh Lotfi, Mahdi Babaei, Ayda Mashhadizadeh, Samira Nikpour, Morteza Bagheri

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The main objective of this study is to investigate the key factors in accidents’ severity at rail-road level crossings. The data required for this study is obtained from both accident and inventory database of Iran Railways during 2009-2015. The Ordered Probit model is developed using SPSS software to identify the significant factors in the accident severity at rail-road level crossings. The results show that 'train speed', 'vehicle type' and 'weather' are the most important factors affecting the severity of the accident. The results of these studies assist to allocate resources in the right place. This paper suggests mandating the regulations to reduce train speed at rail-road level crossings in bad weather conditions to improve the safety of rail-road level crossings.

Keywords: rail-road level crossing, ordered probit model, accidents’ severity, significant factors

Procedia PDF Downloads 116

Authors: Amira Balaska, Ali Tahri, Amine Boudghene Stambouli, Takashi Oozeki

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This paper is dealing with the evaluation of photovoltaic modules as part of the Sahara Solar Breeder project (SSB), five different photovoltaic module technologies which are: m-si, CIS, HIT, Back Contact, a-si_μc -si and a weather station recently installed at the University of Saida (Tahar Moulay) in Saida city located at the gate of the great southern Algeria’s Sahara. The objective of the present work is the study of solar photovoltaic capacity and performance parameters of each PV module technology. The goal of the study is to compare the five different PV technologies in order to find which technologies are suitable for the climate conditions of Algeria’s desert. Measurements of various parameters as irradiance, temperature, humidity and so on by the weather station and I-V curves were performed outdoors at the location without shadow. Finally performance parameters as performance ratio, energy yield and temperature losses are given and analyzed.

Keywords: photovoltaic modules, performance ratio, energy yield, sahara solar breeder, outdoor conditions

Procedia PDF Downloads 632

Authors: Hung-Shuo Wu, Huan-Chieh Chiu, Xiang-Yao Zheng, Yu-Cheng Yang, Chien-Hao Wang, Jen-Cheng Wang, Chwan-Lu Tseng, Joe-Air Jiang

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Dynamic Thermal Rating (DTR) provides crucial information by estimating the ampacity of transmission lines to improve power dispatching efficiency. To perform the DTR, it is necessary to install on-line thermal sensors to monitor conductor temperature and weather variables. A simple and intuitive strategy is to allocate a thermal sensor to every span of transmission lines, but the cost of sensors might be too high to bear. To deal with the cost issue, a thermal sensor placement problem must be solved. This research proposes and implements a hybrid algorithm which combines proper orthogonal decomposition (POD) with particle swarm optimization (PSO) methods. The proposed hybrid algorithm solves a multi-objective optimization problem that concludes the minimum number of sensors and the minimum error on conductor temperature, and the optimal sensor placement is determined simultaneously. The data of 345 kV transmission lines and the hourly weather data from the Taiwan Power Company and Central Weather Bureau (CWB), respectively, are used by the proposed method. The simulated results indicate that the number of sensors could be reduced using the optimal placement method proposed by the study and an acceptable error on conductor temperature could be achieved. This study provides power companies with a reliable reference for efficiently monitoring and managing their power grids.

Keywords: dynamic thermal rating, proper orthogonal decomposition, particle swarm optimization, sensor placement, smart grid

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Authors: B. G. Ayantunji, B. Musa, H. Mai-Unguwa, L. A. Sunmonu, A. S. Adewumi, L. Sa'ad, A. Kado

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For achieving reliable and efficient communication system, both terrestrial and satellite communication, surface refractivity is critical in planning and design of radio links. This study analyzed the impact of atmospheric parameters on Ultra High Frequency (UHF) signal strength over Gusau, North West, Nigeria. The analysis exploited meteorological data measured simultaneously with UHF signal strength for the month of June 2017 using a Davis Vantage Pro2 automatic weather station and UHF signal strength measuring devices respectively. The instruments were situated at the premise of Federal University, Gusau (6° 78' N, 12° 13' E). The refractivity values were computed using ITU-R model. The result shows that the refractivity value attained the highest value of 366.28 at 2200hr and a minimum value of 350.66 at 2100hr local time. The correlation between signal strength and refractivity is 0.350; Humidity is 0.532 and a negative correlation of -0.515 for temperature.

Keywords: refractivity, UHF (ultra high frequency) signal strength, free space, automatic weather station

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Authors: Amir AlAmeeri, Mohamed Ibrahim

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Various data points are constantly being measured in the production system, and due to the nature of the wells, these data points, such as pressure, temperature, water cut, etc.., fluctuations are constant, which requires high frequency monitoring and collection. It is a very difficult task to analyze these parameters manually using spreadsheets and email. An automated system greatly enhances efficiency, reduce errors, the need for constant emails which take up disk space, and frees up time for the operator to perform other critical tasks. Various production data is being recorded in an oil field, and this huge volume of data can be seen as irrelevant to some, especially when viewed on its own with no context. In order to fully utilize all this information, it needs to be properly collected, verified and stored in one common place and analyzed for surveillance and monitoring purposes. This paper describes how data is recorded by different parties and departments in the field, and verified numerous times as it is being loaded into a repository. Once it is loaded, a final check is done before being entered into a production monitoring system. Once all this is collected, various calculations are performed to report allocated production. Calculated production data is used to report field production automatically. It is also used to monitor well and surface facility performance. Engineers can use this for their studies and analyses to ensure field is performing as it should be, predict and forecast production, and monitor any changes in wells that could affect field performance.

Keywords: automation, oil production, Cheleken, exploration and production (E&P), Caspian Sea, allocation, forecast

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Bright Msukwa

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Background: Malawi suffered economic instability, bad weather and massive flooding in the year 2015. A massive flood in the country, mainly in the southern region lead to damage of agriculture products. As a result, one of the heavily affected was Zingwangwa, Blantyre. Methods: We interviewed a selected number of houses residing in donor constructed temporal shelters and those still residing close to the floods prone areas in Zingwangwa, Blantyre. Results: About 67% of the population insisted that they resided on the land, which was prone to the floods as it belonged to their ancestors and their staying was part of preserving ancestral values. The remaining 23% of the population demonstrated economic challenges due to floods that contributed to the damage of their food crops, property and houses. Conclusion: Beliefs can negatively affect economic life improvement if mindsets are not changed among people in the rural area. Recommendation: Improving natural resource management, climate and disaster resilience.

Keywords: economic, belief, walfare, poverty

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Authors: Picha Sriprachan

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The increase in electricity prices affects the cost of high-rise building construction. The objectives of this research are to study the electrical cost, trend of electrical cost and to forecast electrical cost of high-rise building construction. The methods of this research are: 1) to study electrical payment formats, cost data collection methods, and the factors affecting electrical cost of high-rise building construction, 2) to study the quantity and trend of cumulative percentage of the electrical cost, and 3) to forecast the electrical cost for different types of high-rise buildings. The results of this research show that the average proportion between electrical cost and the value of the construction project is 0.87 percent. The proportion of electrical cost for residential, office and commercial, and hotel buildings are closely proportional. If construction project value increases, the proportion of electrical cost and the value of the construction project will decrease. However, there is a relationship between the amount of electrical cost and the value of the construction project. During the structural construction phase, the amount of electrical cost will increase and during structural and architectural construction phase, electrical cost will be maximum. The cumulative percentage of the electrical cost is related to the cumulative percentage of the high-rise building construction cost in the same direction. The amount of service space of the building, number of floors and the duration of the construction affect the electrical cost of construction. The electrical cost of construction forecasted by using linear regression equation is close to the electrical cost forecasted by using the proportion of electrical cost and value of the project.

Keywords: high-rise building construction, electrical cost, construction phase, architectural phase

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: Nicola Colaninno, Eugenio Morello

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The urban environment affects local-to-global climate and, in turn, suffers global warming phenomena, with worrying impacts on human well-being, health, social and economic activities. Physic-morphological features of the built-up space affect urban air temperature, locally, causing the urban environment to be warmer compared to surrounding rural. This occurrence, typically known as the Urban Heat Island (UHI), is normally assessed by means of air temperature from fixed weather stations and/or traverse observations or based on remotely sensed Land Surface Temperatures (LST). The information provided by ground weather stations is key for assessing local air temperature. However, the spatial coverage is normally limited due to low density and uneven distribution of the stations. Although different interpolation techniques such as Inverse Distance Weighting (IDW), Ordinary Kriging (OK), or Multiple Linear Regression (MLR) are used to estimate air temperature from observed points, such an approach may not effectively reflect the real climatic conditions of an interpolated point. Quantifying local UHI for extensive areas based on weather stations’ observations only is not practicable. Alternatively, the use of thermal remote sensing has been widely investigated based on LST. Data from Landsat, ASTER, or MODIS have been extensively used. Indeed, LST has an indirect but significant influence on air temperatures. However, high-resolution near-surface air temperature (NSAT) is currently difficult to retrieve. Here we have experimented Geographically Weighted Regression (GWR) as an effective approach to enable NSAT estimation by accounting for spatial non-stationarity of the phenomenon. The model combines on-site measurements of air temperature, from fixed weather stations and satellite-derived LST. The approach is structured upon two main steps. First, a GWR model has been set to estimate NSAT at low resolution, by combining air temperature from discrete observations retrieved by weather stations (dependent variable) and the LST from satellite observations (predictor). At this step, MODIS data, from Terra satellite, at 1 kilometer of spatial resolution have been employed. Two time periods are considered according to satellite revisit period, i.e. 10:30 am and 9:30 pm. Afterward, the results have been downscaled at 30 meters of spatial resolution by setting a GWR model between the previously retrieved near-surface air temperature (dependent variable), the multispectral information as provided by the Landsat mission, in particular the albedo, and Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM), both at 30 meters. Albedo and DEM are now the predictors. The area under investigation is the Metropolitan City of Milan, which covers an area of approximately 1,575 km2 and encompasses a population of over 3 million inhabitants. Both models, low- (1 km) and high-resolution (30 meters), have been validated according to a cross-validation that relies on indicators such as R2, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). All the employed indicators give evidence of highly efficient models. In addition, an alternative network of weather stations, available for the City of Milano only, has been employed for testing the accuracy of the predicted temperatures, giving and RMSE of 0.6 and 0.7 for daytime and night-time, respectively.

Keywords: urban climate, urban heat island, geographically weighted regression, remote sensing

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Authors: Arun Kumar Singh, Rupesh M. Das, Shailendra Saini

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The present work is an attempt to evaluate the sub-auroral ionospheric behavior under changing space weather conditions especially during high solar activity year 2014. In view of this, the GPS TEC along with Ionosonde data over Indian permanent scientific base 'Maitri', Antarctica (70°46′00″ S, 11°43′56″ E) has been utilized. The results suggested that the nature of ionospheric responses to the geomagnetic disturbances mainly depended upon the status of high latitudinal electro-dynamic processes along with the season of occurrence. Fortunately, in this study, both negative and positive ionospheric impact to the geomagnetic disturbances has been observed in a single year but in different seasons. The study reveals that the combination of equator-ward plasma transportation along with ionospheric compositional changes causes a negative ionospheric impact during summer and equinox seasons. However, the combination of pole-ward contraction of the oval region along with particle precipitation may lead to exhibiting positive ionospheric response during the winter season. Other than this, some Ionosonde based new experimental evidence also provided clear evidence of particle precipitation deep up to the low altitudinal ionospheric heights, i.e., up to E-layer by the sudden and strong appearance of E-layer at 100 km altitudes. The sudden appearance of E-layer along with a decrease in F-layer electron density suggested the dominance of NO⁺ over O⁺ at a considered region under geomagnetic disturbed condition. The strengthening of E-layer is responsible for modification of auroral electrojet and field-aligned current system. The present study provided a good scientific insight on sub-auroral ionospheric to the changing space weather condition.

Keywords: high latitude ionosphere, space weather, geomagnetic storms, sub-storm

Procedia PDF Downloads 138

Authors: Yasser Shabana, Khaled Ghoneem, Nehal Arafat, Younes Rashad, Dalia Aseel, Bruce Fitt, Aiming Qi, Benjamine Richard

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Providing food for more than 100 million people is one of Egypt's main challenges facing development. The overall goal is to formulate strategies to enhance food security in light of population growth. Two hundred samples of maize grains from 25 governates were collected. For the detection of seed-borne fungi, the deep-freezing blotter method (DFB) and washing method (ISTA 1999) were used. A total of 41 fungal species was recovered from maize seed samples. Weather data from 30 stations scattered all over Egypt and covering the major maize growing areas were obtained. Canonical correspondence analysis of data for the obtained fungal genera with temperature, relative humidity, precipitation, wind speed, or solar radiation revealed that relative humidity, temperature and wind speed were the most influential weather variables.

Keywords: biodiversity, climate change, maize, seed-borne fungi

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Authors: Ivana Čavlina Tomašević, Višnjica Vučetić, Maja Telišman Prtenjak, Barbara Malečić

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The Split wildfire on the mid-Adriatic Coast in July 2017 is one of the most severe wildfires in Croatian history, given the size and unexpected fire behavior, and it is used in this research as a case study to run the Weather Research and Forecasting Spread Fire (WRF SFIRE) model. This coupled fire-atmosphere model was successfully run for the first time ever for one Croatian wildfire case. Verification of coupled simulations was possible by using the detailed reconstruction of the Split wildfire. Specifically, precise information on ignition time and location, together with mapped fire progressions and spotting within the first 30 hours of the wildfire, was used for both – to initialize simulations and to evaluate the model’s ability to simulate fire’s propagation and final fire scar. The preliminary simulations were obtained using high-resolution vegetation and topography data for the fire area, additionally interpolated to fire grid spacing at 33.3 m. The results demonstrated that the WRF SFIRE model has the ability to work with real data from Croatia and produce adequate results for forecasting fire spread. As the model in its setup has the ability to include and exclude the energy fluxes between the fire and the atmosphere, this was used to investigate possible fire-atmosphere interactions during the Split wildfire. Finally, successfully coupled simulations provided the first numerical evidence that a wildfire from the Adriatic coast region can modify the dynamical structure of the surrounding atmosphere, which agrees with observations from fire grounds. This study has demonstrated that the WRF SFIRE model has the potential for operational application in Croatia with more accurate fire predictions in the future, which could be accomplished by inserting the higher-resolution input data into the model without interpolation. Possible uses for fire management in Croatia include prediction of fire spread and intensity that may vary under changing weather conditions, available fuels and topography, planning effective and safe deployment of ground and aerial firefighting forces, preventing wildland-urban interface fires, effective planning of evacuation routes etc. In addition, the WRF SFIRE model results from this research demonstrated that the model is important for fire weather research and education purposes in order to better understand this hazardous phenomenon that occurs in Croatia.

Keywords: meteorology, agrometeorology, fire weather, wildfires, couple fire-atmosphere model

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

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Authors: Surachai Ampawasuvan, Supornchai Utainarumol

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Most of fuel station’ customers, who drive on the major arterial road wants to use the stations to fill fuel to their vehicle during their journey to destinations. According to the survey of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, or questionnaires, it was found that most users prefer to use fuel stations on holiday rather than on working day. They also prefer to use fuel stations in the morning rather than in the evening. When comparing the ratio of the distribution pattern of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, there is no significant difference. However, when comparing the ratio of peak hour (peak hour rate) of the results from questionnaires at 13 to 14 percent with the results obtained by using the methods of the Institute of Transportation Engineering (ITE), it is found that the value is similar. However, it is different from a survey by video camera and automatic traffic counting at 6 to 7 percent of about half. So, this study suggests that in order to forecast trip generation of vehicle using fuel stations on major arterial road which is mostly characterized by Though Traffic, it is recommended to use the value of half of peak hour rate, which would make the forecast for trips generation to be more precise and accurate and compatible to surrounding environment.

Keywords: peak rate, trips generation, fuel station, arterial road

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Raul Chavez Jr.

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Green roof's body of knowledge appears to be in its infancy stage in the Philippines. To contribute to its development, this study intends to answer the question: Does the existing green roof in Metro Manila perform well in providing thermal comfort and satisfaction to users? Relatively, this study focuses on thermal sensation and satisfaction of users, surface temperature comparison, weather data comparison of the site (Sky Garden) and local weather station (PAG-ASA), and its thermal resistance capacity. Initially, the researcher conducted a point-in-time survey in parallel with weather data gathering from PAG-ASA and Sky Garden. In line with these, ambient and surface temperature are conducted through the use of a digital anemometer, with humidity and temperature, and non-contact infrared thermometer respectively. Furthermore, to determine the Sky Garden's overall thermal resistance, materials found on site were identified and tabulated based on specified locations. It revealed that the Sky Garden can be considered comfortable based from PMV-PPD Model of ASHRAE Standard 55 having similar results from thermal comfort and thermal satisfaction survey, which is contrary to the actual condition of the Sky Garden by means of a psychrometric chart which falls beyond the contextualized comfort zone. In addition, ground floor benefited the most in terms of lower average ambient temperature and humidity compared to the Sky Garden. Lastly, surface temperature data indicates that the green roof portion obtained the highest average temperature yet performed well in terms of heat resistance compared to other locations. These results provided the researcher valuable baseline information of the actual performance of a certain green roof in Metro Manila that could be vital in locally enhancing the system even further and for future studies.

Keywords: Green Roof, Thermal Analysis, Thermal Comfort, Thermal Performance

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Authors: Omaymah AlAzhari

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Nomadic tribes have always had the need to relocate and build shelters, moving from one site to another in search of food, water, and natural resources. They are affected by weather and seasonal changes and consequently started innovating textiles to build better shelters. Their solutions came from the observation of their natural environment, material, and surroundings. The textile innovation of nomadic tribes has led designers to create environmentally responsive products, such as Ceginskas Lindström’s new self-shading tent membrane developed by her ‘smocking’ technique. ‘AlRahala’ Nomadic Bedouin tribes from the Middle East and North African region have used textiles as a fundamental architectural element in their tent structure, ‘Bayt AlShar’ (House of Hair). The nomadic tribe has innovated their textile to create a fabric that is more suited to change in climatic and weather conditions. Based on the research of existing literature and documents, as well as analysis of photographs and videos, to conclude that the traditional textiles and innovations done by nomadic tribes may be a rich source of information for designers, which can provide innovative solutions for manufacturing modern-day textiles.

Keywords: ‘AlRahala’ nomadic tribes, ‘Bayt AlShar’, tent structure, textile innovation

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Authors: Nattapong Techarattanased

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The purposes of this research are to make comparisons in respect of the behaviors on the use of the services of metered taxi classified by the demographic factor and to study the influence of the recognition on service quality having the effect on usage behaviors of metered taxi services of consumers in Bangkok Metropolitan Areas. The samples used in this research are 400 metered taxi service users in Bangkok Metropolitan Areas and use a questionnaire as the tool for collecting the data. Analysis statistics is mean and multiple regression analysis. Results of the research revealed that the consumers recognize the overall quality of services in each aspect include tangible aspects of the service, responses to customers, assurance on the confidence, understanding and knowing of customers which is rated at the moderate level except the aspect of the assurance on the confidence and trustworthiness which are rated at a high level. For the result of a hypothetical test, it is found that the quality in providing the services on the aspect of the assurance given to the customers has the effect on the usage behaviors of metered taxi services and the aspect of the frequency on the use of the services per month which in this connection. Such variable can forecast at one point nine percent (1.9%). In addition, quality in providing the services and the aspect of the responses to customers have the effect on the behaviors on the use of metered taxi services on the aspect of the expenses on the use of services per month which in this connection, such variable can forecast at two point one percent (2.1%).

Keywords: consumer behavior, metered taxi service, satisfaction, service quality

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Authors: Pedro Miguel Cardoso Carneiro, Ricardo André Nunes Borges, João Pedro Soares Loureiro, Hermínio Maio Graça Fernandes

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Wind energy has been exponentially growing over the last years and will allow countries to progress regarding the decarbonization objective. In parallel, the maintenance activities have also been increasing in consequence of ageing and deterioration of the wind farms. The time available for wind blade maintenance is given by the weather window that is based upon weather conditions. Most of the wind blade repair and maintenance activities require a narrow window of temperature and humidity. Due to this limitation, the current weather windows result only on approximately 35% days/year are used for maintenance, that takes place mostly during summertime. This limitation creates large economic losses in the energy production of the wind towers, since they can be inoperative or with the energy production output reduced for days or weeks due to existing damages. Another important aspect is that the maintenance costs are higher due to the high standby time and seasonality imposed on the technicians. To reduce the relevant maintenance costs of blades and energy loses some technological developments were carried out to significantly improve this reality. The focus of this activity was to develop a series of key developments to have in the near future a suspended access equipment that can operate in harsh conditions, wind rain, cold/hot environment. To this end we have identified key areas that need to be revised and require new solutions to be found; a habitat system, multi-configurable roof and floor, roof and floor interface to blade, secondary attachment solutions to the blade and to the tower. On this paper we will describe the advances produced during a national R&D project made in partnership with an end-user (Onrope) and a test center (ISQ).

Keywords: wind turbine maintenance, cost reduction, technological innovations, wind turbine blade

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Authors: Lu Anne A. Godinez, May Claudine I. Gador, Preacious G. Gumolon, Louiechi Von R. Mendoza, Neil Bryan N. Moninio

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Gender equality and women empowerment is the third of eight Millennium Development Goals. Understanding corruption’s linkages to gender equality issues and how it impacts women’s empowerment is part of the broader process of advancing women’s rights and understanding the gender dimensions of democratic governance. Taking a long view of political (corruption index) and the social (women empowerment) dimension — a view from 2015 to 2030, a context based forecast was conducted to forecast the ASEAN corruption index in the next 15 years, answering the question: “Does women political involvement decrease corruption rate index of ASEAN countries in the next 15 years?” The study have established that there will be an increase women political involvement in the ASEAN countries in the next 15 years that will cause a drop on corruption rate index. There will be a significant decline on corruption rate index in 2030. This change entails reform not only in the political aspect of progress, but to the social aspect as well. Finally, the political aspect is increasing at a constant rate however a double or triple increase of the social aspect is seen to be the key solution for corruption.

Keywords: women, women political involvement, corruption, gender equity index, economic participation, educational attainment, political empowerment, control of corruption, regulatory quality, rule of law, voice and accountability government effectiveness, political stability and corruption perception index

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Authors: Retno A. Vinarti, Lucy M. Hederman

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This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree.

Keywords: epidemiology, knowledge modelling, infectious disease, prediction, risk

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Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Corentin Jankowiak, Aggelos Zacharopoulos, Caterina Brandoni

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In a context of increasing stress put on the electricity network by the decarbonization of many sectors, energy storage is likely to be the key mitigating element, by acting as a buffer between production and demand. In particular, the highest potential for storage is when connected closer to the loads. Yet, low voltage storage struggles to penetrate the market at a large scale due to the novelty and complexity of the solution, and the competitive advantage of fossil fuel-based technologies regarding regulations. Strong and reliable numerical simulations are required to show the benefits of storage located near loads and promote its development. The present study was restrained from excluding aggregated control of storage: it is assumed that the storage units operate independently to one another without exchanging information – as is currently mostly the case. A computationally light battery model is presented in detail and validated by direct comparison with a domestic battery operating in real conditions. This model is then used to develop Peak-Shaving (PS) control strategies as it is the decentralized service from which beneficial impacts are most likely to emerge. The aggregation of flatter, peak- shaved consumption profiles is likely to lead to flatter and arbitraged profile at higher voltage layers. Furthermore, voltage fluctuations can be expected to decrease if spikes of individual consumption are reduced. The crucial part to achieve PS lies in the charging pattern: peaks depend on the switching on and off of appliances in the dwelling by the occupants and are therefore impossible to predict accurately. A performant PS strategy must, therefore, include a smart charge recovery algorithm that can ensure enough energy is present in the battery in case it is needed without generating new peaks by charging the unit. Three categories of PS algorithms are introduced in detail. First, using a constant threshold or power rate for charge recovery, followed by algorithms using the State Of Charge (SOC) as a decision variable. Finally, using a load forecast – of which the impact of the accuracy is discussed – to generate PS. A performance metrics was defined in order to quantitatively evaluate their operating regarding peak reduction, total energy consumption, and self-consumption of domestic photovoltaic generation. The algorithms were tested on load profiles with a 1-minute granularity over a 1-year period, and their performance was assessed regarding these metrics. The results show that constant charging threshold or power are far from optimal: a certain value is not likely to fit the variability of a residential profile. As could be expected, forecast-based algorithms show the highest performance. However, these depend on the accuracy of the forecast. On the other hand, SOC based algorithms also present satisfying performance, making them a strong alternative when the reliable forecast is not available.

Keywords: decentralised control, domestic integrated batteries, electricity network performance, peak-shaving algorithm

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Authors: Patricia Kermeci

Abstract:

Policies related to the reduction of both carbon dioxide and energy consumption within the residential sector have contributed towards a growing number of energy-efficient houses being built in several countries. Many of these energy-efficient houses rely on the construction of very well insulated and highly airtight structures, ventilated mechanically. Although energy-efficient houses are indeed more energy efficient than conventional houses, concerns have been raised over the quality of their indoor air and, consequently, the possible adverse health and wellbeing effects for their occupants. Using a longitudinal study design over three different weather seasons (winter, spring and summer), this study has investigated the indoor climate and indoor air quality of different rooms (bedroom, living room and kitchen) in five energy-efficient houses and four conventional houses in the UK. Occupants have kept diaries of their activities during the studied periods and interviews have been conducted to investigate possible behavioural explanations for the findings. Data has been compared with reviews of epidemiological, toxicological and other health related published literature to reveals three main findings. First, it shows that the indoor environment quality of energy-efficient houses cannot be treated as a holistic entity as different rooms presented dissimilar indoor climate and indoor air quality. Thus, such differences might contribute to the health and wellbeing of occupants in different ways. Second, the results show that the indoor environment quality of energy-efficient houses can vary following changes in weather season, leaving occupants at a lower or higher risk of adverse health and wellbeing effects during different weather seasons. Third, one cannot assume that even identical energy-efficient houses provide a similar indoor environment quality. Fourth, the findings reveal that the practices and behaviours of the occupants of energy-efficient houses likely determine whether they enjoy a healthier indoor environment when compared with their control houses. In conclusion, it has been considered vital to understand occupants’ practices and behaviours in order to explain the ways they might contribute to the indoor climate and indoor air quality in energy-efficient houses.

Keywords: energy-efficient house, health and wellbeing, indoor environment, indoor air quality

Procedia PDF Downloads 198

Authors: Iulia E. Falcan

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The transition of the European power sector towards a clean, renewable energy (RE) system faces the challenge of meeting power demand in times of low wind speed and low solar radiation, at a reasonable cost. This is likely to be achieved through a combination of 1) energy storage technologies, 2) development of the cross-border power grid, 3) installed overcapacity of RE and 4) dispatchable power sources – such as biomass. This paper uses NASA; derived hourly data on weather patterns of sixteen European countries for the past twenty-five years, and load data from the European Network of Transmission System Operators-Electricity (ENTSO-E), to develop a stochastic optimization model. This model aims to understand the synergies between the four classes of technologies mentioned above and to determine the optimal configuration of the energy technologies portfolio. While this issue has been addressed before, it was done so using deterministic models that extrapolated historic data on weather patterns and power demand, as well as ignoring the risk of an unbalanced grid-risk stemming from both the supply and the demand side. This paper aims to explicitly account for the inherent uncertainty in the energy system transition. It articulates two levels of uncertainty: a) the inherent uncertainty in future weather patterns and b) the uncertainty of fully meeting power demand. The first level of uncertainty is addressed by developing probability distributions for future weather data and thus expected power output from RE technologies, rather than known future power output. The latter level of uncertainty is operationalized by introducing a Conditional Value at Risk (CVaR) constraint in the portfolio optimization problem. By setting the risk threshold at different levels – 1%, 5% and 10%, important insights are revealed regarding the synergies of the different energy technologies, i.e., the circumstances under which they behave as either complements or substitutes to each other. The paper concludes that allowing for uncertainty in expected power output - rather than extrapolating historic data - paints a more realistic picture and reveals important departures from results of deterministic models. In addition, explicitly acknowledging the risk of an unbalanced grid - and assigning it different thresholds - reveals non-linearity in the cost functions of different technology portfolio configurations. This finding has significant implications for the design of the European energy mix.

Keywords: cross-border grid extension, energy storage technologies, energy system transition, stochastic portfolio optimization

Procedia PDF Downloads 143

Authors: Rajendra Roul

Abstract:

The main objective of the project is to bring attention towards the performance of an aerofoil when exposed to the fluid medium inside the wind tunnel. This project aims at involvement of civil as well as mechanical engineering thereby making itself as a multidisciplinary project. The airfoil of desired size is taken into consideration for the project to carry out effectively. An aerofoil is the shape of the wing or blade of propeller, rotor or turbine. Lot of experiment have been carried out through wind-tunnel keeping aerofoil as a reference object to make a future forecast regarding the design of turbine blade, car and aircraft. Lift and drag now become the major identification factor for any design industry which shows that wind tunnel testing along with software analysis (ANSYS) becomes the mandatory task for any researchers to forecast an aerodynamics design. This project is an initiative towards the mitigation of drag, better lift and analysis of wake surface profile by investigating the surface pressure distribution. The readings has been taken on airfoil model in Wind Tunnel Testing Machine (WTTM) at different air velocity 20m/sec, 25m/sec, 30m/sec and different angle of attack 00,50,100,150,200. Air velocity and pressures are measured in several ways in wind tunnel testing machine by use to measuring instruments like Anemometer and Multi tube manometer. Moreover to make the analysis more accurate Ansys fluent contribution become substantial and subsequently the CFD simulation results. Analysis on an Aerofoil have a wide spectrum of application other than aerodynamics including wind loads in the design of buildings and bridges for structural engineers.

Keywords: wind-tunnel, aerofoil, Ansys, multitube manometer

Procedia PDF Downloads 380