Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

Krechowicz, Maria; Krechowicz, Adam; Lichołai, Lech; Pawelec, Artur; Piotrowski, Jerzy Zbigniew; Stępień, Anna

doi:10.3390/en15114006

Cited by 14 publications

(7 citation statements)

References 53 publications

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“…Ref. [21] used seven machine learning models to predict the power generation of PV plants. The above seven machine learning models can achieve the fitting of PV power generation.…”

Section: Probability Prediction Prediction Formmentioning

confidence: 99%

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High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Du,

Peng,

et al. 2024

Energies

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Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

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“…Ref. [21] used seven machine learning models to predict the power generation of PV plants. The above seven machine learning models can achieve the fitting of PV power generation.…”

Section: Probability Prediction Prediction Formmentioning

confidence: 99%

“…. , q n h T (19) For a training set containing n m samples, the input matrix X * and output matrix Y * are as shown in Equations ( 20) and (21).…”

Section: Extreme Learning Machinementioning

confidence: 99%

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Du,

Peng,

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…Additionally, mathematical models cannot provide accurate results because they require many coefficients and sophisticated computations [15]. As a result, traditional methodologies cannot provide precise forecasts when dealing with the enormous volume of data generated by new power grids [16].…”

Section: Introductionmentioning

confidence: 99%

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

Min,

Hong,

Song

et al. 2024

Electronics

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We present SolarFlux Predictor, a novel deep-learning model designed to revolutionize photovoltaic (PV) power forecasting in South Korea. This model uses a self-attention-based temporal convolutional network (TCN) to process and predict PV outputs with high precision. We perform meticulous data preprocessing to ensure accurate data normalization and outlier rectification, which are vital for reliable PV power data analysis. The TCN layers are crucial for capturing temporal patterns in PV energy data; we complement them with the teacher forcing technique during the training phase to significantly enhance the sequence prediction accuracy. By optimizing hyperparameters with Optuna, we further improve the model’s performance. Our model incorporates multi-head self-attention mechanisms to focus on the most impactful temporal features, thereby improving forecasting accuracy. In validations against datasets from nine regions in South Korea, SolarFlux outperformed conventional methods. The results indicate that SolarFlux is a robust tool for optimizing PV systems’ management and operational efficiency and can contribute to South Korea’s pursuit of sustainable energy solutions.

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“…The world is consuming more energy, which raises the risk of a worldwide energy crisis with detrimental impacts on the environment [1]. Since the world's raw material supplies (fossil fuels) have significantly decreased, causing severe economic, political, and social concerns, the effective use of energy is a topic that has attracted a lot of attention [2]. It is crucial to have accurate and persistent forecasting to improve power production from renewable energy sources such as water, wind, and solar in accordance with the electricity requirement.…”

Section: Introductionmentioning

confidence: 99%

Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU

Zameer,

Jaffar,

Shahid

et al. 2023

PLoS ONE

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Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for every five-minute interval on Amherst weather power station. These models were evaluated using mean absolute error root means square error, mean square error, and mean absolute percentage error. It was observed that horizontal solar irradiance and water saturation deficiency had a highly proportional relationship with Solar PV electricity generation. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed solar farm. Bi-LSTM has performed the best among all models with 0.0135, 0.0315, 0.0012, and 0.1205 values of MAE, RMSE, MSE, and MAPE, respectively. Comparison with the existing methods endorses the use of our proposed RNN variants for higher efficiency, accuracy, and robustness. Multistep-ahead solar energy prediction is also carried out by exploiting hybrids of LSTM, Bi-LSTM, and GRU.

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Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning

Cited by 14 publications

References 53 publications

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU

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