Forecasting Solar Energy Production Using Machine Learning

Vennila, C.; Titus, Anita; Sudha, T. Sri; Sreenivasulu, U.; Reddy, N. Pandu Ranga; Jamal, K.; Lakshmaiah, Dayadi; Jagadeesh, P.; Belay, Assefa

doi:10.1155/2022/7797488

Cited by 46 publications

(8 citation statements)

References 18 publications

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“…After three weeks of testing, the system achieved a state prediction accuracy of 93.60%. In their approach, Vennila et al [28] propose a hybrid model that integrates machine learning and statistical techniques to improve the accuracy of predicting solar energy production. The model also helps in reducing placement costs by emphasizing the significance of feature selection in forecasting.…”

Section: Deepmentioning

confidence: 99%

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Elhabyb,

Baina,

Bellafkih

et al. 2024

International Journal of Energy Research

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In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building’s energy consumption.

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Section: Deepmentioning

confidence: 99%

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Elhabyb,

Baina,

Bellafkih

et al. 2024

International Journal of Energy Research

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“…Supervised learning (SL) deals with tasks where both the input and output are known during the initial stage. Some studies have utilized supervised learning for their energy study and optimization [109][110][111][112][113][114][115][116][117][118][119][120][121][122]. In supervised learning, classification and regression tasks correspond to predicting discrete and continuous outputs, respectively.…”

Section: Inorganic Pcmsmentioning

confidence: 99%

Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses

Alghamdi,

Maduabuchi,

Okoli

et al. 2024

International Journal of Energy Research

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In recent times, the significance of renewable energy generation has increased and photovoltaic-thermoelectric (PV-TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV-TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic-thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV-TE technologies, offering precise insights to guide upcoming studies and innovations.

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“…Machine learning methods have been widely used in PV power generation. To improve prediction performance, significant attention has lately been drawn to SVM and deep learning algorithms [8]. For example, Pan et al optimized the support vector machine (SVM) by using the global search ability of the ant colony algorithm (ACO), which greatly improved the prediction accuracy of the model, but the ant colony algorithm is easy to fall into local optimum [9].…”

Section: Related Workmentioning

confidence: 99%

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

Tian

Fan

Wang

et al. 2022

Mathematical Problems in Engineering

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The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions.

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Forecasting Solar Energy Production Using Machine Learning

Cited by 46 publications

References 18 publications

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

Latest Advancements in Solar Photovoltaic-Thermoelectric Conversion Technologies: Thermal Energy Storage Using Phase Change Materials, Machine Learning, and 4E Analyses

A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

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