Machine learning algorithms for photovoltaic system power output prediction

Theocharides, Spyros; Makrides, George; Georghiou, George E.; Kyprianou, Andreas

doi:10.1109/energycon.2018.8398737

Cited by 46 publications

(38 citation statements)

References 12 publications

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“…Theocharides et al, [32] examined the performance of three different ML methods, namely ANNs, SVR, and Regression Trees (RTs), with different hyper-parameters and sets of features, in predicting the power production of PV systems. Their success was related to the Persistence model throughout the computation of Mean Absolute Percentage Error (MAPE) and normalized Root Mean Square Error (nRMSE).…”

Section: B Literature Review and Motivationmentioning

confidence: 99%

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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Power production prediction from Renewable Energy (RE) sources has been widely studied in the last decade. This is extremely important for utilities to counterpart electricity supply with consumer demands across centralized grid networks. In this context, we propose a local training strategy-based Artificial Neural Network (ANN) for predicting the power productions of solar Photovoltaic (PV) systems. Specifically, the timestamp, weather variables, and corresponding power productions collected locally at each hour interval h, h=[1,24] (i.e., an interval of ∆ℎ=1 hour), are exploited to build, optimize, and evaluate H=24 different ANNs for the 24 hourly solar PV production predictions. The proposed local training strategy-based ANN is expected to provide more accurate predictions with short computational times than those obtained by a single (i.e., H=1) ANN model (hereafter called benchmark) built, optimized, and evaluated globally on the entire available dataset. The proposed strategy is applied to a case study regarding a 264kWp solar PV system located in Amman, Jordan, and its effectiveness compared to the benchmark is verified by resorting to different performance metrics from the literature. Further, its effectiveness is verified and compared when Extreme Learning Machines (ELMs) are adopted instead of the ANNs, and when the Persistence model is used. The prediction performance of the two training strategies-based ANN is also investigated and compared in terms of i) different weather conditions (i.e., seasons) experienced by the solar PV system under study and ii) different hour intervals (i.e., ∆ℎ=2, 3, and 4 hours) used for partitioning the overall dataset and, thus, establishing the different ANNs (i.e., H =12, 8, and 6 models, respectively).

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Section: B Literature Review and Motivationmentioning

confidence: 99%

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

2020

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“…Therefore, the classification tree and SVM present high performance for the considered sample data. Table 3 summarizes the performance of the classification algorithms during the training in terms of TPR, AUC, and accuracy as defined by Equation (11). In Table 3, it is clear that the SVM classifier presents the highest accuracy with a value of 87.5%.…”

Section: Performance Of the Classification Algorithms During The Traimentioning

confidence: 99%

“…Therefore, the ML classification algorithms can improve the accuracy of the PV modeling based on the traditional SDM and DDM models to the different solar irradiance and temperature. The potential of error reduction is estimated between 0.04% and 0.15% Table 3 summarizes the performance of the classification algorithms during the training in terms of TPR, AUC, and accuracy as defined by Equation (11). In Table 3, it is clear that the SVM classifier presents the highest accuracy with a value of 87.5%.…”

Section: Performance Of the Classification Algorithms During The Traimentioning

confidence: 99%

“…In the last years, machine learning (ML) techniques have been widely adopted to estimate the long-term performances, and predict the output power, of PV plants [9,10]. Theocharides et al [11] assessed the PV generation using three ML techniques, such as artificial neural network (ANN), support vector machine (SVM), and regression tree. The results proved that the ANN presents high accuracy compared to other examined algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Malvoni

Chaibi

2020

Electronics

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It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.

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“…The output prediction efficiency of the models was tested on actual PV production data and evaluated based on absolute percentage error (MAPE), and normalised core mean square error (nRMSE). A comparative analysis showed that artificial neural networks were the best because of the smallest errors (Theocharides et al, 2018). To predict PV production, Gligor, Dumitru and Grif used artificial intelligence, which predicted and managed the production of a PV power plant located in the central part of Romania.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of forecasting methods in production engineering

Winkowski

2019

Engineering Management in Production and Services

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Business management is a continuous decision-making process. It is difficult to imagine a company that does not use forecasting techniques. Even small enterprises without relevant forecasting departments more or less consciously anticipate future events, forecasting the volume of production and setting directions for development. Today’s production companies must quickly adapt to changing customer requirements, implementing structural and technological changes and delivering projects related to the production of new products. Under the dynamically changing conditions, the functioning and effective management of modern enterprises depend on future-oriented information. This increases the validity of forecasting. This article aimed to identify forecasting methods and areas of their use in production engineering. The publications on this subject were reviewed in the Scopus database, using the time frame from January 1970 to June 2018. An original classification of research subareas was created using VOS viewer software, and then, a bibliometric map was developed to visualise the results of the word coexistence analysis. The analysis of the co-occurrence and co-classification of words made it possible to indicate research subareas of forecasting in production engineering and related emerging research areas and issues.

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Machine learning algorithms for photovoltaic system power output prediction

Cited by 46 publications

References 12 publications

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

A Local Training Strategy-Based Artificial Neural Network for Predicting the Power Production of Solar Photovoltaic Systems

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

Classification of forecasting methods in production engineering

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