Solar photovoltaic power prediction using artificial neural network and multiple regression considering ambient and operating conditions

Energy Conversion and Management, Год журнала: 2023, Номер 288, С. 117186 - 117186

Опубликована: Май 18, 2023

Язык: Английский

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A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning

Heliyon, Год журнала: 2024, Номер 10(3), С. e25407 - e25407

Опубликована: Фев. 1, 2024

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era remarkable research innovation. This review article thoroughly examines the recent advancements in field, focusing on interplay between PV systems water within framework AI ML applications, along it analyses current to identify significant patterns, obstacles, prospects this interdisciplinary field. Furthermore, incorporation methods improving performance systems. includes raising their efficiency, implementing predictive maintenance strategies, enabling real-time monitoring. It also explores transformative influence intelligent algorithms techniques, specifically addressing concerns pertaining energy usage, scalability, environmental sustainability. provides thorough analysis literature, identifying areas where is lacking suggesting potential future avenues for investigation. These have resulted increased decreased expenses, improved sustainability system. By utilizing artificial intelligence freshwater productivity can increase by 10 % efficiency. offers informative perspectives researchers, engineers, policymakers involved renewable technology. sheds light latest desalination, which are facilitated ML. The aims guide towards more sustainable technologically advanced future.

Язык: Английский

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Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation

Applied Energy, Год журнала: 2024, Номер 363, С. 123064 - 123064

Опубликована: Март 23, 2024

Язык: Английский

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Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

Renewable Energy, Год журнала: 2024, Номер 226, С. 120437 - 120437

Опубликована: Апрель 1, 2024

Язык: Английский

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Forecasting Solar Energy: Leveraging Artificial Intelligence and Machine Learning for Sustainable Energy Solutions

Journal of Economic Surveys, Год журнала: 2025, Номер unknown

Опубликована: Янв. 21, 2025

ABSTRACT Integrating solar energy into power grids is essential for advancing a low‐carbon economy, but accurate forecasting remains challenging due to output variability. This study comprehensively reviews models, focusing on how Artificial Intelligence (AI) and Machine Learning (ML) enhance forecast accuracy. It examines the current landscape of forecasting, identifies limitations in existing underscores need more adaptable approaches. The primary goals are analyze evolution AI/ML‐based assess their strengths weaknesses, propose structured methodology selecting implementing AI/ML models tailored forecasting. Through comparative analysis, evaluates individual hybrid across different scenarios, identifying under‐explored research areas. findings indicate significant improvements prediction accuracy through advancements, aiding grid management supporting transition. Ensemble methods, deep learning techniques, show great promise enhancing reliability. Combining diverse approaches with advanced techniques results reliable forecasts. suggests that improving model these integrated methods offers substantial opportunities further research, contributing global sustainability efforts, particularly UN SDGs 7 13, promoting economic growth minimal environmental impact.

Язык: Английский

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Artificial Neural Networks for Photovoltaic Power Forecasting: A Review of Five Promising Models

IEEE Access, Год журнала: 2024, Номер 12, С. 90461 - 90485

Опубликована: Янв. 1, 2024

Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV forecasts are increasingly crucial for managing controlling integrated systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase accuracy of various geographical regions. Hence, this paper provides a state-of-the-art review five most popular ANN forecasting. These include multilayer perceptron (MLP), recurrent (RNN), long short-term memory (LSTM), gated unit (GRU), convolutional (CNN). First, internal structure operation these studied. It then followed by brief discussion main factors affecting their forecasting accuracy, including horizons, meteorological evaluation metrics. Next, an in-depth separate analysis standalone hybrid provided. has determined that bidirectional GRU LSTM offer greater whether used as model or configuration. Furthermore, upgraded metaheuristic algorithms demonstrated exceptional performance when applied models. Finally, study discusses limitations shortcomings may influence practical implementation

Язык: Английский

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Renewable energy sources integration via machine learning modelling: A systematic literature review

Heliyon, Год журнала: 2024, Номер 10(4), С. e26088 - e26088

Опубликована: Фев. 1, 2024

The use of renewable energy sources (RESs) at the distribution level has become increasingly appealing in terms costs and technology, expecting a massive diffusion near future placing several challenges to power grid. Since RESs depend on stochastic —solar radiation, temperature wind speed, among others— they introduce high uncertainty grid, leading imbalance deteriorating network stability. In this scenario, managing forecasting RES is vital successfully integrate them into grids. Traditionally, physical- statistical-based models have been used predict outputs. Nevertheless, former are computationally expensive since rely solving complex mathematical atmospheric dynamics, whereas latter usually consider linear models, preventing from addressing challenging scenarios. recent years, advances machine learning techniques, which can learn historical data, allowing analysis large-scale datasets either under non-uniform characteristics or noisy provided researchers with powerful data-driven tools that outperform traditional methods. paper, systematic literature review conducted identify most widely learning-based approaches forecast results show deep artificial neural networks, especially long-short term memory accurately model autoregressive nature output, ensemble strategies, allow handling large amounts highly fluctuating best suited ones. addition, promising integrating forecasted output decision-making problems, such as unit commitment, address economic, operational managerial grid discussed, solid directions for research provided.

Язык: Английский

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Enhancing solar photovoltaic energy production prediction using diverse machine learning models tuned with the chimp optimization algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 10, 2024

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared predict production based on four independent weather variables: wind speed, relative humidity, ambient temperature, solar irradiation. The evaluated include multiple linear regression (MLR), decision tree (DTR), random forest (RFR), support vector (SVR), multi-layer perceptron (MLP). These were hyperparameter tuned using chimp optimization algorithm (ChOA) a performance appraisal. subsequently validated data from 264 kWp PV system, installed at Applied Science University (ASU) Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with corresponding value 0.503, followed by absolute (MAE) 0.397 coefficient determination (R

Язык: Английский

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An interpretable horizontal federated deep learning approach to improve short-term solar irradiance forecasting

Journal of Cleaner Production, Год журнала: 2024, Номер 436, С. 140585 - 140585

Опубликована: Янв. 1, 2024

Язык: Английский

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Modeling of high uncertainty photovoltaic generation in quasi dynamic power flow on distribution systems: A case study in Java Island, Indonesia

Results in Engineering, Год журнала: 2024, Номер 21, С. 101747 - 101747

Опубликована: Янв. 5, 2024

Integrating uncertainties associated with photovoltaic (PV) generation is an important aspect used to ensure the planning and operation of power distribution systems. Therefore, this research proposed uncertainty model for PV by combining methods change point detection, cyclic k-means clustering (KMC), Monte Carlo simulation (MCS) freedman diaconis estimator (FDE), KMC soft-dynamic time warping (DTW). Firstly, a seasonal split was performed using detection techniques identify shifts in global horizontal irradiance (GHI) points. Secondly, GHI generated MCS each season FDE method optimize number bins data distribution. Finally, curve from simplified through soft-DTW metric, which facilitated more straightforward representation profile. The impact profile integration on quasi-dynamic flow tested IEEE 33 Bus system. voltage feeder significantly impacted integration, specifically during hours when high produced. For instance, at 11:00 a.m., values buses 18, 17, increased 0.933, 0.934, 0.935, respectively, 0.982, 0.980, 0.972. Similarly, value losses, greater produced certain hour, smaller losses generated. experimental results indicated that changes electrical parameters over according input

Язык: Английский

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