Search results for: hybrid forecasting model

Authors: Maha Hammami, Olfa Benouda Sioud

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This study investigates the reliability of management earnings forecasts with reference to these two ingredients: verifiability and neutrality. Specifically, we examine the biasedness (or accuracy) of management earnings forecasts and company specific characteristics that can be associated with accuracy. Based on sample of 102 IPO prospectuses published for admission on NYSE Euronext Paris from 2002 to 2010, we found that these forecasts are on average optimistic and two of the five test variables, earnings variability and financial leverage are significant in explaining ex post bias. Acknowledging the possibility that the bias is the result of the managers’ forecasting behavior, we then examine whether managers decide to under-predict, over-predict or forecast accurately for self-serving purposes. Explicitly, we examine the role of financial distress, operating performance, ownership by insiders and the economy state in influencing managers’ forecasting preferences. We find that managers of distressed firms seem to over-predict future earnings. We also find that when managers are given more stock options, they tend to under-predict future earnings. Finally, we conclude that the management earnings forecasts are affected by an intentional bias due to managers’ forecasting preferences.

Keywords: intentional bias, management earnings forecasts, neutrality, verifiability

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Authors: Hamid Goharnejad, Amir Hossein Eghbali

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Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One models of Discrete Wavelet artificial Neural Network (DWNN) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and predictands to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 to 105 cm. Furthermore the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management.

Keywords: climate change scenarios, sea-level rise, strait of Hormuz, forecasting

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Authors: Alaa Koaik, Bruno Jurkiewiez, Sylvain Bel

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Recently, the use of GFRP pultruded profiles increased in the domain of civil engineering especially in the construction of sandwiched slabs and footbridges. However, under heavy loads, the risk of using these profiles increases due to their high deformability and instability as a result of their weak stiffness and orthotropic nature. A practical solution proposes the assembly of these profiles with concrete slabs to create a stiffer hybrid element to support higher loads. The connection of these two elements is established either by traditional means of steel studs (bolting in our case) or bonding technique. These two techniques have their advantages and disadvantages regarding the mechanical behavior and in-situ implementation. This paper presents experimental results of interface characterization and bending behavior of two hybrid beams, PB7 and PB8, designed and constructed using both connection techniques. The results obtained are exploited to design and build a hybrid footbridge BPBP1 which is tested within service limits (elastic domain). Analytical methods are also developed to analyze the behavior of these structures in the elastic range and the ultimate phase. Comparisons show acceptable differences mainly due to the sensitivity of the GFRP moduli as well as the non-linearity of concrete elements.

Keywords: analytical model, concrete, flexural behavior, GFRP pultruded profile, hybrid structure, interconnection slip, push-out

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Authors: Ibrahim Ozkan

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In this study, a cross-layer design which combines adaptive modulation and coding (AMC) and hybrid automatic repeat request (HARQ) techniques for a cooperative wireless network is investigated analytically. Previous analyses of such systems in the literature are confined to the case where the fading channel is independent at each retransmission, which can be unrealistic unless the channel is varying very fast. On the other hand, temporal channel correlation can have a significant impact on the performance of HARQ systems. In this study, utilizing a Markov channel model which accounts for the temporal correlation, the performance of non-cooperative and cooperative networks are investigated in terms of packet loss rate and throughput metrics for Chase combining HARQ strategy.

Keywords: cooperative network, adaptive modulation and coding, hybrid ARQ, correlated fading

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Authors: Jiangyong Liu, Xiangxiang Xu, Bote Luo, Xiaoxue Luo, Jiang Zhu, Lingzhi Yi

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To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the Least Square Support Vector Machine optimized by an Improved Sparrow Search Algorithm combined with the Variational Mode Decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of Intrinsic Mode Functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the least Square Support Vector Machine. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy.

Keywords: load forecasting, variational mode decomposition, improved sparrow search algorithm, least square support vector machine

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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

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Authors: Julius Agaka Yusufu

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This study undertakes the development of an optimal PV-diesel hybrid power system tailored to the specific energy landscape of Vimtim Mubi, Nigeria, utilizing real-world wind speed, solar radiation, and diesel cost data. Employing HOMER simulation, the research meticulously assesses the technical and financial viability of this hybrid configuration. Additionally, a rigorous performance comparison is conducted between the PV-diesel system and the conventional grid-connected alternative, offering crucial insights into the potential advantages and economic feasibility of adopting hybrid renewable energy solutions in regions grappling with energy access and reliability challenges, with implications for sustainable electrification efforts in similar communities worldwide.

Keywords: Vimtim-Nigeria, homer, renewable energy, PV-diesel hybrid system.

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Authors: Djamel Saba, Fatima Zohra Laallam, Brahim Berbaoui

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This paper presents a contribution to the design of a multi-agent for the energy management system in a hybrid energy system (SEH). The multi-agent-based energy-coordination management system (MA-ECMS) is based mainly on coordination between agents. The agents share the tasks and exchange information through communications protocols to achieve the main goal. This intelligent system can fully manage the consumption and production or simply to make proposals for action he thinks is best. The initial step is to give a presentation for the system that we want to model in order to understand all the details as much as possible. In our case, it is to implement a system for simulating a process control of energy management.

Keywords: communications protocols, control process, energy management, hybrid energy system, modelization, multi-agents system, simulation

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Authors: William Albeiro Alvarez, Gloria Patricia Jaramillo, Ivan Reinaldo Sarmiento

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Understanding the behavior of individuals and the different human factors that influence the choice when faced with a complex system such as transportation is one of the most complicated aspects of measuring in the components that constitute the modeling of route choice due to that various behaviors and driving mode directly or indirectly affect the choice. During the last two decades, with the development of information and communications technologies, new data collection techniques have emerged such as GPS, geolocation with mobile phones, apps for choosing the route between origin and destination, individual service transport applications among others, where an interest has been generated to improve discrete choice models when considering the incorporation of these developments as well as psychological factors that affect decision making. This paper implements a discrete choice model that proposes and estimates a hybrid model that integrates route choice models and latent variables based on the observation on the route of a sample of public taxi drivers from the city of Medellín, Colombia in relation to its behavior, personality, socioeconomic characteristics, and driving mode. The set of choice options includes the routes generated by the individual service transport applications versus the driver's choice. The hybrid model consists of measurement equations that relate latent variables with measurement indicators and utilities with choice indicators along with structural equations that link the observable characteristics of drivers with latent variables and explanatory variables with utilities.

Keywords: behavior choice model, human factors, hybrid model, real time data

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Authors: Paul Han, Jun-Geol Baek

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In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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Authors: Nazaitul Idya Hamzah, Muhammad Syafiq Mazli, Maszatul Akmar Mustafa

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The peak demand forecast is crucial to identify the future generation plant up needed in the long-term capacity planning analysis for Peninsular Malaysia as well as for the transmission and distribution network planning activities. Currently, peak demand forecast (in Mega Watt) is derived from the generation forecast by using load factor assumption. However, a forecast using this method has underperformed due to the structural changes in the economy, emerging trends and weather uncertainty. The dynamic changes of these drivers will result in many possible outcomes of peak demand for Peninsular Malaysia. This paper will look into the independent model of peak demand forecasting. The model begins with the selection of driver variables to capture long-term growth. This selection and construction of variables, which include econometric, emerging trend and energy variables, will have an impact on the peak forecast. The actual framework begins with the development of system energy and load shape forecast by using the system’s hourly data. The shape forecast represents the system shape assuming all embedded technology and use patterns to continue in the future. This is necessary to identify the movements in the peak hour or changes in the system load factor. The next step would be developing the peak forecast, which involves an iterative process to explore model structures and variables. The final step is combining the system energy, shape, and peak forecasts into the hourly system forecast then modifying it with the forecast adjustments. Forecast adjustments are among other sales forecasts for electric vehicles, solar and other adjustments. The framework will result in an hourly forecast that captures growth, peak usage and new technologies. The advantage of this approach as compared to the current methodology is that the peaks capture new technology impacts that change the load shape.

Keywords: hourly load profile, load forecasting, long term peak demand forecasting, peak demand

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Authors: Hechmi Saidi, Noureddine Hamdi

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The performance of hybrid amplify and forward - decode and forward (AF-DF) hybrid radio frequency/free space optical (RF/FSO) communication system, that adopts M-ary pulse position modulation (MPPM) techniques, is analyzed. Both exact and approximate symbol-error rates (SERs) are derived. The random variations of the received optical irradiance, produced by the atmospheric turbulence, is modeled by the gamma-gamma (GG) statistical distribution. A closed-form expression for the probability density function (PDF) is derived for the whole above system is obtained. Thanks to the use of hybrid AF-DF hybrid RF/FSO configuration and MPPM, the effects of atmospheric turbulence is mitigated; hence the capacity of combating atmospheric turbulence and the transmissitted signal quality are improved.

Keywords: free space optical, gamma gamma channel, radio frequency, decode and forward, error pointing, M-ary pulse position modulation, symbol error rate

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Authors: M. Taouzari, A. Sardi, J. El Aoufi, Ahmed Mouhsen

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The purpose of this work is to design a reader antenna fed by 90° hybrid coupler that would ensure a tag which is detected regardless of its orientation for the radio frequency identification system covering the UHF and ISM bands frequencies. Based on this idea, the proposed work is dividing in two parts, first part is about study and design hybrid coupler using the resonators planar called T-and Pi networks operating in commercial bands. In the second part, the proposed antenna fed by the hybrid coupler is designed on FR4 substrate with dielectric permittivity 4.4, thickness dielectric 1.6mm and loss tangent 0.025. The T-slot is inserted in patch of the proposed antenna fed by the hybrid coupler is first designed, optimized and simulated using electromagnetic simulator ADS and then simulated in a full wave simulation software CST Microwave Studio. The simulated antenna by the both softwares achieves the expected performances in terms of matching, pattern radiation, phase shifting, gain and size.

Keywords: dual band antenna, RFID, hybrid coupler, polarization, radiation pattern

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Authors: Mahdi Kiani, Hassan Salarieh, Aria Alasty, S. Mahdi Darbandi

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The design and implementation of the hybrid control method for a three-pole active magnetic bearing (AMB) is proposed in this paper. The system is inherently nonlinear and conventional nonlinear controllers are a little complicated, while the proposed hybrid controller has a piecewise linear form, i.e. linear in each sub-region. A state-feedback hybrid controller is designed in this study, and the unmeasurable states are estimated by an observer. The gains of the hybrid controller are obtained by the Linear Quadratic Regulator (LQR) method in each sub-region. To evaluate the performance, the designed controller is implemented on an experimental setup in static mode. The experimental results show that the proposed method can efficiently stabilize the three-pole AMB system. The simplicity of design, domain of attraction, uncomplicated control law, and computational time are advantages of this method over other nonlinear control strategies in AMB systems.

Keywords: active magnetic bearing, three pole AMB, hybrid control, Lyapunov function

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Behzad Ali Khan, M. Zubair Akbar Qureshi

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The article analyzes the Darcy Forchheimer 2D Hybrid ferrofluid. The flow of a Hybrid ferrofluid is made due to an unsteady porous channel. The classical liquid water is treated as a based liquid. The flow in the permeable region is characterized by the Darcy-Forchheimer relation. Heat transfer phenomena are studied during the flow. The transformation of a partial differential set of equations into a strong ordinary differential frame is formed through appropriate variables. The numerical Shooting Method is executed for solving the simplified set of equations. In addition, a numerical analysis (ND-Solve) is utilized for the convergence of the applied technique. The influence of some flow model quantities like Pr (Prandtle number), r (porous medium parameter), F (Darcy-porous medium parameter), Re (Reynolds number), Pe (Peclet number) on velocity and temperature field are scrutinized and studied through sketches. Certain physical factors like f ''(η) (skin friction coefficient) and θ^'(η) (rate of heat transfer) are first derived and then presented through tables.

Keywords: darcy forcheimer, hybrid ferro fluid, porous medium, porous channel

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Authors: Tehreem Fatima, Enjun Xia

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The research investigates the relationship between total oil consumption and transport oil consumption, GDP, oil price, and oil reserve in order to forecast future oil demand in China. Annual time series data is used over the period of 1980 to 2015, and for this purpose, an oil demand function is estimated by applying structural time series model (STSM). The technique also uncovers the Underline energy demand trend (UEDT) for China oil demand and GDP, oil reserve, oil price and UEDT are considering important drivers of China oil demand. The long-run elasticity of total oil consumption with respect to GDP and price are (0.5, -0.04) respectively while GDP, oil reserve, and price remain (0.17; 0.23; -0.05) respectively. Moreover, the Estimated results of long-run elasticity of transport oil consumption with respect to GDP and price are (0.5, -0.00) respectively long-run estimates remain (0.28; 37.76;-37.8) for GDP, oil reserve, and price respectively. For both model estimated underline energy demand trend (UEDT) remains nonlinear and stochastic and with an increasing trend of (UEDT) and based on estimated equations, it is predicted that China total oil demand somewhere will be 9.9 thousand barrel per day by 2025 as compare to 9.4 thousand barrel per day in 2015, while transport oil demand predicting value is 9.0 thousand barrel per day by 2020 as compare to 8.8 thousand barrel per day in 2015.

Keywords: china, forecasting, oil, structural time series model (STSM), underline energy demand trend (UEDT)

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Authors: Julius Agaka Yusufu, Tsutomu Dei, Hanif Ibrahim Awal

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This study undertakes the development of an optimal PV-diesel hybrid power system tailored to the specific energy landscape of Vimtim Mubi, Nigeria, utilizing real-world wind speed, solar radiation, and diesel cost data. Employing HOMER simulation, the research meticulously assesses the technical and financial viability of this hybrid configuration. Additionally, a rigorous performance comparison is conducted between the PV-diesel system and the conventional grid-connected alternative, offering crucial insights into the potential advantages and economic feasibility of adopting hybrid renewable energy solutions in regions grappling with energy access and reliability challenges, with implications for sustainable electrification efforts in similar communities worldwide.

Keywords: Vimtim-Nigeria, Homer, renewable energy, PV-diesel hybrid system

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Authors: Dmytro Zubov, Francesco Volponi

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In this paper, D-Wave quantum computing Ising model is used for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data respectively. Ising model’s real-valued weights and dimensionless coefficients are calculated using daily mean air temperatures from 119 places around the world, as well as sea level (Aburatsu, Japan). In comparison with current methods, this approach is better suited to predict heat wave values because it does not require the estimation of a probability distribution from scarce observations. Proposed forecast quantum computing algorithm is simulated based on traditional computer architecture and combinatorial optimization of Ising model parameters for the Ronald Reagan Washington National Airport dataset with 1-day lead-time on learning sample (1975-2010 yr). Analysis of the forecast accuracy (ratio of successful predictions to total number of predictions) on the validation sample (2011-2014 yr) shows that Ising model with three qubits has 100 % accuracy, which is quite significant as compared to other methods. However, number of identified heat waves is small (only one out of nineteen in this case). Other models with 2, 4, and 5 qubits have 20 %, 3.8 %, and 3.8 % accuracy respectively. Presented three-qubit forecast model is applied for prediction of heat waves at other five locations: Aurel Vlaicu, Romania – accuracy is 28.6 %; Bratislava, Slovakia – accuracy is 21.7 %; Brussels, Belgium – accuracy is 33.3 %; Sofia, Bulgaria – accuracy is 50 %; Akhisar, Turkey – accuracy is 21.4 %. These predictions are not ideal, but not zeros. They can be used independently or together with other predictions generated by different method(s). The loss of human life, as well as environmental, economic, and material damage, from extreme air temperatures could be reduced if some of heat waves are predicted. Even a small success rate implies a large socio-economic benefit.

Keywords: heat wave, D-wave, forecast, Ising model, quantum computing

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Authors: Nur Suraya Anis Ahmad Bakhtiar, Hazizan Md Akil

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In the present work, the dielectric properties of Epoxy/MWCNTs-muscovite HYBRID and MIXED composites based on ratio 30:70 were studies. The multi-wall carbon nanotubes (MWCNTs) were prepared by two method; (a) muscovite-MWCNTs hybrids were synthesized by chemical vapor deposition (CVD) and (b) physically mixing of muscovite with MWCNTs. The effect of different preparations of the composites and filler loading was evaluated. It is revealed that the dielectric constants of HYBRID epoxy composites are slightly higher compared to MIXED epoxy composites. It is also indicated that the dielectric constant increased by increases the MWCNTs filler loading.

Keywords: muscovite, epoxy, dielectric properties, hybrid composite

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Authors: Amar Patnaik, Pankaj Agarwal

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This paper investigates the experimental study of hardness and compressive strength of hybrid glass/steel fiber reinforced polymer composites by varying the glass and steel fiber layer in the epoxy matrix. The hybrid composites with four stacking sequences HSG-1, HSG-2, HSG-3, and HSG-4 were fabricated by the VARTM process under the controlled environment. The experimentally evaluated results of Vicker’s hardness of the fabricated composites increases with an increase in the fiber layers sequence showing the high resistance. The improvement of micro-structure ability has been observed from the SEM study, which governs in the enhancement of compressive strength. The finite element model was developed on ANSYS to predict the above said properties and further compared with experimental results. The results predicted by the numerical simulation are in good agreement with the experimental results. The hybrid composites developed in this study was identified as the preferred materials due to their excellent mechanical properties to replace the conventional materialsused in the marine structures.

Keywords: finite element method, interfacial strength, polymer composites, VARTM

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Authors: Osung Im, Neha Yadav, Eui Hoon Lee, Joong Hoon Kim

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Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data.

Keywords: artificial neural network, forecasting, reservoir inflow, sewer depth

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Authors: Arpan Dwivedi, Yogesh Pahariya

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In this paper, optimal design of hybrid standalone power supply system (SAPS) is done for off grid applications in remote areas where transmission of power is difficult. The hybrid SAPS system uses two primary energy sources, wind and solar, and in addition to these diesel generator is also connected to meet the load demand in case of failure of wind and solar system. This paper presents mathematical modeling of 1.4 MW hybrid SAPS system for rural electrification. This paper firstly focuses on mathematical modeling of PV module connected in a string, secondly focuses on modeling of permanent magnet wind turbine generator (PMWTG). The hybrid controller is also designed for selection of power from the source available as per the load demand. The power output of hybrid SAPS system is analyzed for meeting load demands at urban as well as for rural areas.

Keywords: SAPS, DG, PMWTG, rural area, off-grid, PV module

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Authors: Reda Dzingelevičienė, Vytautas Abromaitis, Nerijus Dzingelevičius, Kęstutis Baranauskis, Saulius Raugelė, Malgorzata Mlynska-Szultka, Sergej Suzdalev, Reza Pashaei, Sajjad Abbasi, Boguslaw Buszewski

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A unique hybrid technology was developed for the removal of COVID-19 specific contaminants from wastewater. Reactor testing was performed using model water samples contaminated with COVID-19 pharmaceuticals and microplastics. Different hydraulic retention times, concentrations of pollutants and dissolved ozone were tested. Liquid Chromatography-Mass Spectrometry, solid phase extraction, surface area and porosity, analytical tools were used to monitor the treatment efficiency and remaining sorption capacity of the spent adsorbent. The combination of advanced oxidation and adsorption processes was found to be the most effective, with the highest 90-99% and 89-95% molnupiravir and microplastics contaminants removal efficiency from the model wastewater. The research has received funding from the European Regional Development Fund (project No 13.1.1-LMT-K-718-05-0014) under a grant agreement with the Research Council of Lithuania (LMTLT), and it was funded as part of the European Union’s measure in response to the COVID-19 pandemic.

Keywords: adsorption, hybrid reactor system, pharmaceuticals-microplastics, wastewater

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: S. O. Ologe, U. P. Anaidhuno, Duru C. A.

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This research investigated the design and analysis of a rear bumper of an automobile with a hybrid polymer composite of OPEBF/Banana fibre. OPEBF/Banana fibre hybrid polymers composite is of low cost, lightweight, as well as possesses satisfactory mechanical properties. In this research work, hybrid composites have been developed using the hand layup technique based on the percentage combination of OPEBF/Banana fibre at 10:90, 20:80, 30:70, 40:60, 50:50. 60:40, 70:30. 20:80, 90:10, 95:5. The mechanical properties in the context of compressive strength of 65MPa, a flexural strength of 20MPa, and impact strength of 3.25Joule were observed, and the simulation analysis on the induction of 500N load at the factor of safety of 3 was observed to have displayed a good strength suitable for automobile bumper with the advantages of weight reduction.

Keywords: OPEBF, Banana, fibre, hybrid

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Authors: Anwar U. Haque, Waqar Asrar, Ashraf Ali Omar, Erwin Sulaeman, Jaffer Sayed Mohamed Ali

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Due to the interference effects, the intrinsic aerodynamic parameters obtained from the individual component testing are always fundamentally different than those obtained for complete model testing. Consideration and limitation for such testing need to be taken into account in any design work related to the component buildup method. In this paper, the scaled model of a straight rectangular canard of a hybrid buoyant aircraft is tested at 50 m/s in IIUM-LSWT (Low-Speed Wind Tunnel). Model and its attachment with the balance are kept rigid to have results free from the aeroelastic distortion. Based on the velocity profile of the test section’s floor; the height of the model is kept equal to the corresponding boundary layer displacement. Balance measurements provide valuable but limited information of the overall aerodynamic behavior of the model. Zero lift coefficient is obtained at -2.2o and the corresponding drag coefficient was found to be less than that at zero angles of attack. As a part of the validation of low fidelity tool, the plot of lift coefficient plot was verified by the experimental data and except the value of zero lift coefficient, the overall trend has under-predicted the lift coefficient. Based on this comparative study, a correction factor of 1.36 is proposed for lift curve slope obtained from the panel method.

Keywords: wind tunnel testing, boundary layer displacement, lift curve slope, canard, aerodynamics

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Authors: Christian Castro Samaniego, Daniel Icaza Alvarez, Juan Portoviejo Brito

Abstract:

For this research work, hybrid wind-photovoltaic (SHEF) systems were considered as renewable energy sources that take advantage of wind energy and solar radiation to transform into electrical energy. In the present research work, the feasibility of a wind-photovoltaic hybrid generation system was analyzed for the La Tranca tourist viewpoint of the Chordeleg canton in Ecuador. The research process consisted of the collection of data on solar radiation, temperature, wind speed among others by means of a meteorological station. Simulations were carried out in MATLAB/Simulink based on a mathematical model. In the end, we compared the theoretical radiation-power curves and the measurements made at the site.

Keywords: hybrid system, wind turbine, modeling, simulation, validation, experimental data, panel, Ecuador

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Authors: R. Ramesh, K. K. Shivaraman

Abstract:

The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

Procedia PDF Downloads 281