Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

Dec, Grzegorz; Drałus, Grzegorz; Kwiatkowski, Bogdan

doi:10.3390/en14061676

Cited by 14 publications

(11 citation statements)

References 30 publications

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“…23 The learning methods comprised the Artificial Neural Network (ANN), 24,25 Support Vector Machine (SVM), 26 Wavelet Analysis (WA), 27 and Fuzzy Logic (FL). 28 The ANN is deemed one of the most popular statistical methods adopted to predict the PV generation with a prediction horizon of 24-h ahead. 29,30 For example, Kushwaha and Pindoriya 31 adopted the Seasonal ARIMA (SARIMA) model for multi-step ahead power production prediction of solar PV systems installed in IIT Gandhinagar University, India.…”

Section: Literature Reviewmentioning

confidence: 99%

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An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Al‐Dahidi

Muhsen

Sari

et al. 2022

Advances in Mechanical Engineering

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The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Al‐Dahidi

Muhsen

Sari

et al. 2022

Advances in Mechanical Engineering

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“…Renewable energy sources with direct voltage are connected to the network via inverters [17,18], through which it is possible to influence losses in the network. Each inverter has a P-Q (working diagram).…”

Section: Possibilities Of Active Power Loss Reductionmentioning

confidence: 99%

Optimization of Active Power Losses in Smart Grids Using Photovoltaic Power Plants

et al. 2022

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This article addresses the reduction of power losses in smart grids. Two optimization algorithms are used in this article. The first method is the enumerative method. The second method of the optimization calculation is based on the self-organizing migrating algorithm. In the first step, the network parameters are calculated based on the input data, and then the target function is determined. In this article, the target function is used to reduce the active power losses that occur during the operation of an electric network. More specifically, we attempt to determine the reactive power with the enumerative and SOMA algorithms to reduce the value of the active power losses. This article intends to illustrate the differences between the selected optimization algorithms. As observed, the optimization algorithm determines the computation time.

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“…Energy forecasting is an essential component in assuring the optimal operation and development of power systems. Considering the growth of renewable energy sources (RES) and their stochastic nature, generation forecasting represents a topic of high interest in recent years [1][2][3]. However, the load forecasting continues to be a problem of great importance, as it is involved in many applications, such as economic power dispatch, storage scheduling, or network planning [4].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

et al. 2021

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Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

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Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic

Cited by 14 publications

References 30 publications

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Optimization of Active Power Losses in Smart Grids Using Photovoltaic Power Plants

Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

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