A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks

Hussain, Sajid; Al-Alili, Ali

doi:10.1016/j.apenergy.2017.09.100

Cited by 94 publications

(19 citation statements)

References 43 publications

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“…This section summarizes various articles reported on the suitability of various AI techniques for modeling, sizing, T A B L E 5 Statistical analysis of models 18,32,52,53,68,69 Parameter Formula…”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…(2011b); Radhi and Sharples (2013); Al-Mulali and Tang (2013); Alkhathlan and Javid (2013); Mansouri et al., (2013); Al-Mulali and Ozturk (2014); Salahuddin and Gow (2014); Salahuddin et al. (2015); Jammazi and Aloui (2015); Alshehry and Belloumi (2015); Boräng et al.,2016; Sweidan and Alwaked (2016); Atalla and Hunt (2016); Osman et al., (2016); Hussain and Al-Alili (2016); Ghazal et al., (2016); Charfeddine and Khediri (2016); Bekhet et al., (2017); Hasanov et al., (2017); Mezghani and Haddad (2017); Mahalik et al., (2017); Hussain and AlAlili (2017); Azar and Al Ansari (2017); Salahuddin et al., (2018); Charfeddine et al., (2018)…”

Section: Main Textmentioning

confidence: 99%

Survey and analysis of the quantitative methods used in electricity research on GCC countries: 1983–2018

AlKhars

2019

Heliyon

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This study provides a systematic analysis of research on the electricity sector in Gulf Cooperation Council (GCC) countries in the period 1983–2018. GCC countries have experienced tremendous economic growth in the past few decades. This was accompanied by a corresponding increase in electricity consumption. Therefore, a thorough review is needed to understand the research conducted on the electricity sector in GCC countries. This study reviewed articles published in five well-known energy journals: Applied Energy, Energy, Energy Economics, Energy Policy, and Renewable and Sustainable Energy Reviews. The articles were classified into seven categories based on the analysis tools implemented in the papers: 1. Simulation tools, 2. Scenarios tools, 3. Equilibrium tools, 4. Top-down tools, 5. Bottom-up tools, 6. Operations optimization tools, and 7. Investment optimization tools. This study also provides an overview of the research, including the increase in publications over time, an authorship analysis, a keywords analysis, and an analysis of the length of the publications.

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“…The authors in another study [21] implemented and evaluated an optimized prediction model that was based on ANNs and a genetic algorithm (GA). The authors in yet another study [22] explored four architectures (Adaptive Neuro Fuzzy Inference System (ANFIS), Multilayer Perceptron (MLP), GRNN, and Nonlinear Autoregressive Recurrent Exogenous Neural Network (NARX)) for enhancing the prediction precision. They proposed hybrid wavelet-ANN models for solar forecasting at a specific site.…”

Section: Introductionmentioning

confidence: 99%

Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

2019

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Background: The Distributed Energy Resources (DERs) are beneficial in reducing the electricity bills of the end customers in a smart community by enabling them to generate electricity for their own use. In the past, various studies have shown that owing to a lack of awareness and connectivity, end customers cannot fully exploit the benefits of DERs. However, with the tremendous progress in communication technologies, the Internet of Things (IoT), Big Data (BD), machine learning, and deep learning, the potential benefits of DERs can be fully achieved, although a significant issue in forecasting the generated renewable energy is the intermittent nature of these energy resources. The machine learning and deep learning models can be trained using BD gathered over a long period of time to solve this problem. The trained models can be used to predict the generated energy through green energy resources by accurately forecasting the wind speed and solar irradiance. Methods: We propose an efficient approach for microgrid-level energy management in a smart community based on the integration of DERs and the forecasting wind speed and solar irradiance using a deep learning model. A smart community that consists of several smart homes and a microgrid is considered. In addition to the possibility of obtaining energy from the main grid, the microgrid is equipped with DERs in the form of wind turbines and photovoltaic (PV) cells. In this work, we consider several machine learning models as well as persistence and smart persistence models for forecasting of the short-term wind speed and solar irradiance. We then choose the best model as a baseline and compare its performance with our proposed multiheaded convolutional neural network model. Results: Using the data of San Francisco, New York, and Los Vegas from the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory (NREL) as a case study, the results show that our proposed model performed significantly better than the baseline model in forecasting the wind speed and solar irradiance. The results show that for the wind speed prediction, we obtained 44.94%, 46.12%, and 2.25% error reductions in root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), respectively. In the case of solar irradiance prediction, we obtained 7.68%, 54.29%, and 0.14% error reductions in RMSE, mean bias error (MBE), and sMAPE, respectively. We evaluate the effectiveness of the proposed model on different time horizons and different climates. The results indicate that for wind speed forecast, different climates do not have a significant impact on the performance of the proposed model. However, for solar irradiance forecast, we obtained different error reductions for different climates. This discrepancy is certainly due to the cloud formation processes, which are very different for different sites with different climates. Moreover, a detailed analysis of the generation estimation and electricity bill reduction indicates that the proposed framework will help the smart community to achieve an annual reduction of up to 38% in electricity bills by integrating DERs into the microgrid. Conclusions: The simulation results indicate that our proposed framework is appropriate for approximating the energy generated through DERs and for reducing the electricity bills of a smart community. The proposed framework is not only suitable for different time horizons (up to 4 h ahead) but for different climates.

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A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks

Cited by 94 publications

References 43 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Survey and analysis of the quantitative methods used in electricity research on GCC countries: 1983–2018

Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

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