Search results for: hourly%20charge%20rate

Authors: J. B. G. Ibarra, J. M. A. Gagui, E. J. T. Jonson, J. A. V. Lim

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This paper focused on studying the performance of the solar panels that were equipped with water-spray cooling system, solar tracking system, and combination of both systems. The efficiencies were compared with the solar panels without any efficiency improvement technique. The efficiency of each setup was computed on an hourly basis every day for a month. The study compared the efficiencies and combined systems that significantly improved at a specific time of the day. The data showed that the solar tracking system had the highest efficiency during 6:00 AM to 7:45 AM. Then after 7:45 AM, the combination of both solar tracking and water-spray cooling system was the most efficient to use up to 12:00 NN. Meanwhile, from 12:00 NN to 12:45 PM, the water-spray cooling system had the significant contribution on efficiency. From 12:45 PM up to 4:30 PM, the combination of both systems was the most efficient, and lastly, from 4:30 PM to 6:00 PM, the solar tracking system was the best to use. The study intended to use solar tracking or water-spray cooling system or combined systems alternately to improve the solar panel efficiency on a specific time of the day.

Keywords: solar panel efficiency, solar panel efficiency technique, solar tracking system, water-spray cooling system

Procedia PDF Downloads 135

Authors: Mohamed Youssef

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The proliferation of energy consumption in the built environment has made energy efficiency and savings strategies a priority objective for energy policies in most countries. Qatar is a clear example, where it has initiated several programs and institutions to mitigate the overuse of electricity consumption and control the energy load of the building by following global standards and spreading awareness campaigns. A Case study on the Office of Research and Graduate Studies at Qatar University has been investigated in this paper. The paper studied the rating load of existing buildings before and after retrofitting by using Carrier’s Hourly Analysis Program (HAP). The performance of the building has increased especially after using the LED light system instead of fluorescent light with a low payback period. GINAN paint and green roof have shown a considerable contribution to the reduction of electrical load in the building. In comparison, the double HR window had the least effect on the reduction of electricity consumption.

Keywords: energy conservation in Qatar, HAP, LED light, GINAN paint, green roof, double HR window

Procedia PDF Downloads 143

Authors: Nor Zila Abd Hamid, Mohd Salmi M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: chaotic approach, phase space, Cao method, local linear approximation method

Procedia PDF Downloads 296

Authors: M. R. Dahmardeh Ghaleno, M. Nohtani, S. Khaledi

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Flood simulation and prediction is one of the most active research areas in surface water management. WetSpa is a distributed, continuous, and physical model with daily or hourly time step that explains precipitation, runoff, and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave equation which depends on the slope, velocity, and flow route characteristics. Golestan Dam Basin is located in Golestan province in Iran and it is passing over coordinates 55° 16´ 50" to 56° 4´ 25" E and 37° 19´ 39" to 37° 49´ 28"N. The area of the catchment is about 224 km², and elevations in the catchment range from 414 to 2856 m at the outlet, with average slope of 29.78%. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe model efficiency coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 59% and 80.18%, respectively.

Keywords: watershed simulation, WetSpa, stream flow, flood prediction

Procedia PDF Downloads 221

Authors: H. Rezzouk, M. Hatti, H. Rahmani, S. Atoui

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This paper discusses the design and analysis of a hybrid PV-Fuel cell energy system destined to power a DC load. The system is composed of a photovoltaic array, a fuel cell, an electrolyzer and a hydrogen tank. HOMER software is used in this study to calculate the optimum capacities of the power system components that their combination allows an efficient use of solar resource to cover the hourly load needs. The optimal system sizing allows establishing the right balance between the daily electrical energy produced by the power system and the daily electrical energy consumed by the DC load using a 28 KW PV array, a 7.5 KW fuel cell, a 40KW electrolyzer and a 270 Kg hydrogen tank. The variation of powers involved into the DC bus of the hybrid PV-fuel cell system has been computed and analyzed for each hour over one year: the output powers of the PV array and the fuel cell, the input power of the elctrolyzer system and the DC primary load. Equally, the annual variation of stored hydrogen produced by the electrolyzer has been assessed. The PV array contributes in the power system with 82% whereas the fuel cell produces 18%. 38% of the total energy consumption belongs to the DC primary load while the rest goes to the electrolyzer.

Keywords: electrolyzer, hydrogen, hydrogen fueled cell, photovoltaic

Procedia PDF Downloads 454

Authors: Peregrine James Dalziel, Philip Vu Tran

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Objective / Introduction: Demand for radiology services from Emergency Departments (ED) continues to increase with greater demands placed on radiology staff providing reports for the management of complex cases. Queuing theory indicates that wide variability of process time with the random nature of request arrival increases the probability of significant queues. This can lead to delays in the time-to-availability of radiology reports (TTA-RR) and potentially impaired ED patient flow. In addition, greater “cognitive workload” of greater volume may lead to reduced productivity and increased errors. We sought to quantify the potential ED flow improvements obtainable from increased radiology providers serving 3 public hospitals in Melbourne Australia. We sought to assess the potential productivity gains, quality improvement and the cost-effectiveness of increased labor inputs. Methods & Materials: The Western Health Medical Imaging Department moved from single resident coverage on weekend days 8:30 am-10:30 pm to a limited period of 2 resident coverage 1 pm-6 pm on both weekend days. The TTA-RR for weekend CT scans was calculated from the PACs database for the 8 month period symmetrically around the date of staffing change. A multivariate linear regression model was developed to isolate the improvement in TTA-RR, between the two 4-months periods. Daily and hourly scan volume at the time of each CT scan was calculated to assess the impact of varying department workload. To assess any improvement in report quality/errors a random sample of 200 studies was assessed to compare the average number of clinically significant over-read addendums to reports between the 2 periods. Cost-effectiveness was assessed by comparing the marginal cost of additional staffing against a conservative estimate of the economic benefit of improved ED patient throughput using the Australian national insurance rebate for private ED attendance as a revenue proxy. Results: The primary resident on call and the type of scan accounted for most of the explained variability in time to report availability (R2=0.29). Increasing daily volume and hourly volume was associated with increased TTA-RR (1.5m (p<0.01) and 4.8m (p<0.01) respectively per additional scan ordered within each time frame. Reports were available 25.9 minutes sooner on average in the 4 months post-implementation of double coverage (p<0.01) with additional 23.6 minutes improvement when 2 residents were on-site concomitantly (p<0.01). The aggregate average improvement in TTA-RR was 24.8 hours per weekend day This represents the increased decision-making time available to ED physicians and potential improvement in ED bed utilisation. 5% of reports from the intervention period contained clinically significant addendums vs 7% in the single resident period but this was not statistically significant (p=0.7). The marginal cost was less than the anticipated economic benefit based assuming a 50% capture of improved TTA-RR inpatient disposition and using the lowest available national insurance rebate as a proxy for economic benefit. Conclusion: TTA-RR improved significantly during the period of increased staff availability, both during the specific period of increased staffing and throughout the day. Increased labor utilisation is cost-effective compared with the potential improved productivity for ED cases requiring CT imaging.

Keywords: workflow, quality, administration, CT, staffing

Procedia PDF Downloads 87

Authors: Sergio Perez Rodriguez

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Coltan Artisanal and Small-Scale Mining (ASM) production from Africa's Great Lakes region has previously been addressed at large scales, notably from regional to country levels. The current findings address the unresolved issue of a production model of ASM of coltan ore by an average Democratic Republic of Congo (DRC) mineworker, which can be used as a reference for a similar characterization of the daily labor of counterparts from other countries in the region. To that end, the Fundamental Equation of Mineral Production has been applied, considering a miner's average daily output of coltan, estimated in the base of gross statistical data gathered from reputable sources. Results indicate daily yields of individual miners in the order of 300 g of coltan ore, with hourly peaks of production in the range of 30 to 40 g of the mineral. Yields are expected to be in the order of 5 g or less during the least productive hours. These outputs are expected to be achieved during the halves of the eight to ten hours of daily working sessions that these artisanal laborers can attend during the mining season.

Keywords: coltan, mineral production, production to reserve ratio, artisanal mining, small-scale mining, ASM, human work, Great Lakes region, Democratic Republic of Congo

Procedia PDF Downloads 50

Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

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Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

Procedia PDF Downloads 89

Authors: Ekrem Canli, Thomas Glade

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The precise prediction of rainfall in space and time is a key element to most landslide early warning systems. Unfortunately, the spatial variability of rainfall in many early warning applications is often disregarded. A common simplification is to use uniformly distributed rainfall to characterize aerial rainfall intensity. With spatially differentiated rainfall information, real-time comparison with rainfall thresholds or the implementation in process-based approaches might form the basis for improved landslide warnings. This study suggests an automated workflow from the hourly, web-based collection of rain gauge data to the generation of spatially differentiated rainfall predictions based on kriging. Because the application of kriging is usually a labor intensive task, a simplified and consequently automated variogram modeling procedure was applied to up-to-date rainfall data. The entire workflow was carried out purely with open source technology. Validation results, albeit promising, pointed out the challenges that are involved in pure distance based, automated geostatistical interpolation techniques for ever-changing environmental phenomena over short temporal and spatial extent.

Keywords: kriging, landslide early warning system, spatial rainfall prediction, variogram modelling, web scraping

Procedia PDF Downloads 258

Authors: Tran Le Na, Didit Novianto, Yoshiaki Ushifusa, Weijun Gao

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This paper introduces the characteristics of Japanese residential lifestyle and Japanese Architectural housing design, meanwhile, summarizes the results from an analysis of energy use of 12 households in electric-only multi dwellings in Higashida Smart Community, Kitakyushu, Japan. Using hourly load and daily load data collected from smart meter, we explore correlations of energy use in households according to the incentive of different levels of architectural characteristics and lifestyle, following three factors: Space (Living room, Kitchen, Bedroom, Bathroom), Time (daytime and night time, weekdays and weekend) and User (Elderly, Parents, Kids). The energy consumption reports demonstrated that the essential demand of household’s response to variable factors. From that exploratory analysis, we can define the role of housing equipment layout and spatial layout in residential housing design. Likewise, determining preferred spaces and time use can help to optimize energy consumption in households. This paper contributes to the application of Smart Home Energy Management System in Smart Community in Japan and provides a good experience to other countries.

Keywords: smart community, energy efficiency, architectural housing design, residential lifestyle

Procedia PDF Downloads 177

Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

Procedia PDF Downloads 25

Authors: Rufus Temidayo Akinnubi, M. O. Adeniyi

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Existing diurnal air temperature models simulate night time air temperature over Nigeria with high biases. An improved parameterization is presented for modeling the diurnal pattern of air temperature (Ta) which is applicable in the calculation of turbulent heat fluxes in Global climate models, based on Nigeria Micrometeorological Experimental site (NIMEX) surface layer observations. Five diurnal Ta models for estimating hourly Ta from daily maximum, daily minimum, and daily mean air temperature were validated using root-mean-square error (RMSE), Mean Error Bias (MBE) and scatter graphs. The original Fourier series model showed better performance for unstable air temperature parameterizations while the stable Ta was strongly overestimated with a large error. The model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion that occurs during the nocturnal boundary layer condition. The MBE and RMSE estimated by the modified Fourier series model reduced by 4.45 oC and 3.12 oC during the transitional period from dry to wet stable atmospheric conditions. The modified Fourier series model gave good estimation of the diurnal weather patterns of Ta when compared with other existing models for a tropical environment.

Keywords: air temperature, mean bias error, Fourier series analysis, surface energy balance,

Procedia PDF Downloads 202

Authors: Doaa M. Atia, Faten H. Fahmy, Ninet M. A. El-Rahman, Hassan T. Dorra

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In this paper, the optimum design for renewable energy system powered an aquaculture pond was determined. Hybrid Optimization Model for Electric Renewable (HOMER) software program, which is developed by U.S National Renewable Energy Laboratory (NREL), is used for analyzing the feasibility of the stand-alone and hybrid system in this study. HOMER program determines whether renewable energy resources satisfy hourly electric demand or not. The program calculates energy balance for every 8760 hours in a year to simulate operation of the system. This optimization compares the demand for the electrical energy for each hour of the year with the energy supplied by the system for that hour and calculates the relevant energy flow for each component in the model. The essential principle is to minimize the total system cost while HOMER ensures control of the system. Moreover the feasibility analysis of the energy system is also studied. Wind speed, solar irradiance, interest rate and capacity shortage are the parameters which are taken into consideration. The simulation results indicate that the hybrid system is the best choice in this study, yielding lower net present cost. Thus, it provides higher system performance than PV or wind stand-alone systems.

Keywords: wind stand-alone system, photovoltaic stand-alone system, hybrid system, optimum system sizing, feasibility, cost analysis

Procedia PDF Downloads 313

Authors: Jawdhari Abdulhusein, Dan Mihăilescu, Cristian-Emilian Pop, Nicolai Crăciun

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Cyprinus Carpio adult specimens were captured from a single spot location from the Danube River and transported to the Laboratory of Faculty of Biology, University of Bucharest. The specimens were measured, weighed and randomly divided in five groups for later Copper Sulphate (CuSO4) exposure, as well as one control group. From the control group, 3 specimens were immediately sacrificed and microscopically inspected for parasite’s presence in the gills. During the histological inspection of the control group specimens, Ichthyophthiriusmultifiliis, Gyrodactylu ssp., MyxobolusOviforis, and Trichodinacottidarumwere abundantly identified in the gills. The exposure to a single dose of CuSO4 per group took place for 7 days under controlled conditions: water alkalinity ≈ 100 mg/L, temperature 23–24 °C, oxygenation 7.40–7.80 mg/L, and pH 6.10–6.40, the parameters were monitored on an hourly basis and adjusted when needed. After the exposure, all specimens were sacrificed, and the gill tissues were inspected. It was found that concentrations of 0.10, 0.25 and 0.30 ppm of CuSO4 did not affect the presence of parasites, as for 0.40 ppm of CuSO4 it significantly affected onlyIchthyophthiriusmultifiliisandMyxobolusOviforis, while concentration 0.50 ppm of CuSO4 (≈0.20 ppm of Cu2+) eliminated all 4 parasites species previously present in the fish gills, also, there were some histopathological changes to the gills tissue. These findings suggest that low concentrations of CuSO4 treatment for fish parasites are a safe and economical option if used correctly for a brief length of time.

Keywords: copper sulphate, cyprinus carpio, parasites, danube river

Procedia PDF Downloads 112

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: 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

Procedia PDF Downloads 537

Authors: Sami Mohamed Sharif

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The current study focuses on the design and construction of a solar tunnel dryer intended for small-scale farmers in Kassala, Sudan. To determine the appropriate dimensions of the dryer, the heat and mass balance equations are used, taking into account factors such as the target agricultural product, climate conditions, solar irradiance, and desired drying time. In Kassala, a dryer with a width of 88 cm, length of 600 cm, and height of 25 cm has been built, capable of drying up to 40 kg of vegetables or fruits. The dryer is divided into two chambers of different lengths. The air passing through is heated to the desired drying temperature in a separate heating chamber that is 200 cm long. From there, the heated air enters the drying chamber, which is 400 cm long. In this section, the agricultural product is placed on a slightly elevated net. The tunnel dryer was constructed using materials from the local market. The paper also examines the solar irradiance in Kassala, finding an average of 23.6 MJ/m2/day, with a maximum of 26.6 MJ/m2/day in April and a minimum of 20.2 MJ/m2/day in December. A DC fan powered by a 160Wp solar panel is utilized to circulate air within the tunnel. By connecting the fan and three 12V, 60W bulbs in series, four different speeds can be achieved using a speed controller. Temperature and relative humidity measurements were taken hourly over three days, from 10:00 a.m. to 3:00 p.m. The results demonstrate the promising technology and sizing techniques of solar tunnel dryers, which can significantly increase the temperature within the tunnel by more than 90%.

Keywords: tunnel dryer, solar drying, moisture content, fruits drying modeling, open sun drying

Procedia PDF Downloads 30

Authors: Mohammed Wadi, Bedri Kekezoglu, Mustafa Baysal, Mehmet Rida Tur, Abdulfetah Shobole

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This paper investigates the technical evaluation of the wind potential for present and future investments in Turkey taking into account the feasibility of sites, installments, operation, and maintenance. This evaluation based on the hourly measured wind speed data for the three years 2008–2010 at 30 m height for Çatalca district. These data were obtained from national meteorology station in Istanbul–Republic of Turkey are analyzed in order to evaluate the feasibility of wind power potential and to assure supreme assortment of wind turbines installing for the area of interest. Furthermore, the data are extrapolated and analyzed at 60 m and 80 m regarding the variability of roughness factor. Weibull bi-parameter probability function is used to approximate monthly and annually wind potential and power density based on three calculation methods namely, the approximated, the graphical and the energy pattern factor methods. The annual mean wind power densities were to be 400.31, 540.08 and 611.02 W/m² for 30, 60, and 80 m heights respectively. Simulation results prove that the analyzed area is an appropriate place for constructing large-scale wind farms.

Keywords: wind potential in Turkey, Weibull bi-parameter probability function, the approximated method, the graphical method, the energy pattern factor method, capacity factor

Procedia PDF Downloads 231

Authors: P. K. Singhal, R. Naresh, V. Sharma

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This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: artificial bee colony algorithm, economic dispatch, unit commitment, wind power

Procedia PDF Downloads 351

Authors: Amirreza Heidari, Akram Avami, Ehsan Heidari

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Evaporative cooling systems are known as energy efficient cooling systems, with much lower electricity consumption than conventional vapor compression systems. A serious limitation of these systems, however, is that they are not applicable in humid regions. Combining a desiccant wheel with these systems, known as desiccant-based evaporative cooling systems, makes it possible to use evaporative cooling in humid climates. This paper evaluates the performane of a cooling system combining desiccant wheel, direct and indirect evaporative coolers (called desiccant-based indirect-direct evaporative cooling (DIDE) system) and then evaluates the energy saving potential of this system over the conventional vapor compression cooling and drying system. To illustrate the system ability of providing comfort conditions, a dynamic hourly simulation of this system is performed for a typical 60 m² building in Sydney, Australia. To evaluate the energy saving potential of this system, a conventional cooling and drying system is also simulated for the same cooling capacity. It has been found that the DIE system is able to provide comfort temperature and relative humidity in a subtropical humid climate like Sydney. The electricity and natural gas consumption of this system are respectively 39.2% and 2.6% lower than that of conventional system over a week. As the research has demonstrated, the innovative DIDE system is an energy efficient cooling system for subtropical humid regions.

Keywords: desiccant, evaporative cooling, dehumidification, indirect evaporative cooler

Procedia PDF Downloads 122

Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Marin Mandić, Elis Sutlović, Tonći Modrić, Luka Stanić

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With the restructuring and deregulation of the power system, storage owners, generation companies or private producers can offer their multiple services on various power markets and earn income in different types of markets, such as the day-ahead, real-time, ancillary services market, etc. During the COVID-19 pandemic, electricity prices, as well as ancillary services prices, increased significantly. The optimization of the energy storage operation was performed using a suitable model for simulating the operation of a pumped storage hydropower plant under market conditions. The objective function maximizes the income earned through energy arbitration, regulation-up, regulation-down and spinning reserve services. The optimization technique used for solving the objective function is mixed integer linear programming (MILP). In numerical examples, the pumped storage hydropower plant operation has been optimized considering the already achieved hourly electricity market prices from Nord Pool for the pre-pandemic (2019) and the pandemic (2020 and 2021) years. The impact of the electricity market prices during the COVID-19 pandemic on energy storage operation is shown through the analysis of income, operating hours, reserved capacity and consumed energy for each service. The results indicate the role of energy storage during a significant fluctuation in electricity and services prices.

Keywords: electrical market prices, electricity market, energy storage optimization, mixed integer linear programming (MILP) optimization

Procedia PDF Downloads 140

Authors: C. S. Seema, P. R. Prince

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The characterization of spatio-temporal inhomogeneities occurring in the ionospheric F₂ layer is remarkable since these variations are direct consequences of electrodynamical coupling between magnetosphere and solar events. The temporal and spatial variations of the F₂ layer, which occur with a period of several days or even years, mainly owe to geomagnetic and meteorological activities. The hourly F₂ layer critical frequency (foF2) over 23rd solar cycle (1996-2008) of three ionosonde stations (Wakkanai, Kokunbunji, and Okinawa) in northern hemisphere, which falls within same longitudinal span, is analyzed using continuous wavelet techniques. Morlet wavelet is used to transform continuous time series data of foF2 to a two dimensional time-frequency space, quantifying the time evolution of the oscillatory modes. The presence of significant time patterns (periodicities) at a particular time period and the time location of each periodicity are detected from the two-dimensional representation of the wavelet power, in the plane of scale and period of the time series. The mean strength of each periodicity over the entire period of analysis is studied using global wavelet spectrum. The quasi biennial, annual, semiannual, 27 day, diurnal and 12 hour variations of foF2 are clearly evident in the wavelet power spectra in all the three stations. Critical frequency oscillations with multi-day periods (2-3 days and 9 days in the low latitude station, 6-7 days in all stations and 15 days in mid-high latitude station) are also superimposed over large time scaled variations.

Keywords: continuous wavelet analysis, critical frequency, ionosphere, solar cycle

Procedia PDF Downloads 187

Authors: Muhammad Bilal, Zhongfeng Qiu

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Aerosols, suspended particles in the atmosphere, play an important role in the earth energy budget, climate change, degradation of atmospheric visibility, urban air quality, and human health. To fully understand aerosol effects, retrieval of aerosol optical properties such as aerosol optical depth (AOD) at high spatiotemporal resolution is required. Therefore, in the present study, hourly AOD observations at 500 m resolution were retrieved from the geostationary ocean color imager (GOCI) using the simplified aerosol retrieval algorithm (SARA) over the urban area of Beijing for the year 2016. The SARA requires top-of-the-atmosphere (TOA) reflectance, solar and sensor geometry information and surface reflectance observations to retrieve an accurate AOD. For validation of the GOCI retrieved AOD, AOD measurements were obtained from the aerosol robotic network (AERONET) version 3 level 2.0 (cloud-screened and quality assured) data. The errors and uncertainties were reported using the root mean square error (RMSE), relative percent mean error (RPME), and the expected error (EE = ± (0.05 + 0.15AOD). Results showed that the high spatiotemporal GOCI AOD observations were well correlated with the AERONET AOD measurements with a correlation coefficient (R) of 0.92, RMSE of 0.07, and RPME of 5%, and 90% of the observations were within the EE. The results suggested that the SARA is robust and has the ability to retrieve high-resolution spatiotemporal AOD observations over the urban area using the geostationary satellite.

Keywords: AEORNET, AOD, SARA, GOCI, Beijing

Procedia PDF Downloads 138

Authors: Ajay Agrawal, Pritha Basnet, Achala Thakur, Pappu Rizal, Rubina Rai

Abstract:

Context: Rapid expulsion of fetus in intrauterine fetal death (IUFD) is usually requested without any medical grounds for it. So; an efficient, safe method for induction of labor (IOL) is required. Objective: To determine if pre-treatment with mifepristone followed by IOL with misoprostol in late IUFD is more efficacious. Methods: We conducted a randomized controlled trial in 100 patients. Group-A women received single oral dose of 200 mg mifepristone, followed by induction with vaginal misoprostol after 24-hour. Group-B women were induced only with vaginal misoprostol. In each group 5 dose of misoprostol was used 4 hourly. If first cycle was unsuccessful, after break of 12 hour, second course of misoprostol was started. The primary outcome was a measure of induction to delivery time and vaginal delivery within 24 hours. Secondary outcome was to measure need of oxytocin and complications. Results: Maternal age, parity and period of gestation were comparable between groups. Number of misoprostol dose needed in group A was significantly less than group B. Mann Whitney U test showed, women in group A had significantly earlier onset of labor, however total induction to delivery interval was not significant. In group-A, 85.7% delivered within 24 hours of first dose of misoprostol while in group-B 70% delivered within 24 hour (p=0.07). More women in Group B required oxytocin. Conclusion: Pretreatment with mifepristone before IOL following late IUFD is an effective and safe regimen. It appears to shorten the duration of induction to onset of labor.

Keywords: induction of labor, intrauterine fetal death, mifepristone, misoprostol

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Authors: Giovanni Masala, Stefania Marica

Abstract:

Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

Procedia PDF Downloads 374

Authors: Cheng-Yu Tsai, Wen-Te Liu, Chen-Chen Lo, Yin-Tzu Lin, Kang Lo

Abstract:

Background: Driving fatigue related to inadequate or disordered sleep accounts for a major percentage of traffic accidents. Obstructive sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. However, the effects of OSAS severity on driving drowsiness remain unclear. Objective: The aim of this study is to investigate the relationship between OSAS severity and driving fatigue. Methodologies: The physical condition while driving was obtained from the questionnaires to classify the state of driving fatigue. OSAS severity was quantified as the polysomnography, and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). The severity of OSAS was diagnosed by the apnea and hypopnea index (AHI) with the American Academy of Sleep Medicine (AASM) guideline. The logistic regression model was used to examine the associations after adjusted age, gender, neck circumstance, waist circumstance, and body mass index (BMI). Results: There were 880 subjects recruited in this study, who had been done polysomnography for evaluating severity for OSAS as well as completed the driver condition questionnaire. 752 subjects were diagnosed with OSA, and 484 subjects had fatigue driving behavior in the past week. Patients diagnosed with OSAS had a 9.42-fold higher odds ratio (p < 0.01, 95% CI = 5.41 – 16.42) of driving drowsiness for cohorts with a normal degree. Conclusion: We observe the considerable correlation between OSAS and driving fatigue. For the purpose of promoting traffic safety, OSAS should be monitored and treated.

Keywords: obstructive sleep apnea syndrome, driving fatigue, polysomnography, apnea and hypopnea index

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

Abstract:

Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

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Authors: T. Costa, D. Narvaez, K. Melo, M. Villalva

Abstract:

Installation of micro-generators based on renewable energy in power distribution system has increased in recent years, with the main renewable sources being solar and wind. Due to the intermittent nature of renewable energy sources, such micro-generators produce time-varying energy which does not correspond at certain times of the day to the peak energy consumption of end users. For this reason, the use of energy storage units next to the grid contributes to the proper leveling of the buses’ voltage level according to Brazilian energy quality standards. In this work, the effect of the addition of a photovoltaic solar generator and a store of energy in the busbar voltages of an electric system is analyzed. The consumption profile is defined as the average hourly use of appliances in a common residence, and the generation profile is defined as a function of the solar irradiation available in a locality. The power summation method is validated with analytical calculation and is used to calculate the modules and angles of the voltages in the buses of an electrical system based on the IEEE standard, at each hour of the day and with defined load and generation profiles. The results show that bus 5 presents the worst voltage level at the power consumption peaks and stabilizes at the appropriate range with the inclusion of the energy storage during the night time period. Solar generator maintains improvement of the voltage level during the period when it receives solar irradiation, having peaks of production during the 12 pm (without exceeding the appropriate maximum levels of tension).

Keywords: energy storage, power distribution system, solar generator, voltage level

Procedia PDF Downloads 99

Authors: Cheng-Yu Tsai, Wen-Te Liu, Yin-Tzu Lin, Chen-Chen Lo, Kang Lo

Abstract:

Background: Driving drowsiness related to inadequate or disordered sleep accounts for a major percentage of traffic accidents. Sleep-disordered breathing (SDB) syndrome is a common respiratory disorder during sleep. However, the effects of SDB syndrome on driving fatigue remain unclear. Objective: This study aims to investigate the relationship between SDB pattern and driving drowsiness. Methodologies: The physical condition while driving was obtained from the questionnaires to classify the state of driving fatigue. SDB syndrome was quantified as the polysomnography, and the air flow pattern was collected by the thermistor and nasal pressure cannula. To evaluate the desaturation, the mean hourly number of greater than 3% dips in oxygen saturation was sentenced by reregistered technologist during examination in a hospital in New Taipei City (Taiwan). The independent T-test was used to investigate the correlations between sleep disorders related index and driving drowsiness. Results: There were 880 subjects recruited in this study, who had been done polysomnography for evaluating severity for obstructive sleep apnea syndrome (OSAS) as well as completed the driver condition questionnaire. Four-hundred-eighty-four subjects (55%) were classified as fatigue group, and 396 subjects (45%) were served as the control group. Significantly higher values of snoring index (242.14 ± 205.51 /hours) were observed in the fatigue group (p < 0.01). The value of respiratory disturbance index (RDI) (31.82 ± 19.34 /hours) in fatigue group were significantly higher than the control group (p < 0.01). Conclusion: We observe the considerable association between SDB syndrome and driving drowsiness. To promote traffic safety, SDB syndrome should be controlled and alleviated.

Keywords: driving drowsiness, sleep-disordered breathing syndrome, snoring index, respiratory disturbance index.

Procedia PDF Downloads 108