A novel power output model for photovoltaic system

Kittisontirak, Songkiate; Dawan, Promphak; Atiwongsangthong, Narin; Titiroongruang, Wisut; Chinnavornrungsee, Perawut; Hongsingthong, Aswin; Sriprapha, Kobsak; Manosukritkul, Phasapon

doi:10.1109/ieecon.2017.8075768

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…In [21], a comparison of various sky models from the point of view of solar irradiation forecasting is provided. In [22,23], various models of solar panels were investigated from the point of electrical energy production.…”

Section: Introductionmentioning

confidence: 99%

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

et al. 2020

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This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation.

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Section: Introductionmentioning

confidence: 99%

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

et al. 2020

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Comparison of Performance of 1D5P with 2 Input and 1 Input Model in Forecasting of Power Output for Photovoltaic Systems

Dawan

Kittisontirak

Sriprapha

et al. 2018

2018 International Electrical Engineering Congress (iEECON)

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Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

et al. 2020

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The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

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A novel power output model for photovoltaic system

Cited by 3 publications

References 6 publications

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

Comparison of Performance of 1D5P with 2 Input and 1 Input Model in Forecasting of Power Output for Photovoltaic Systems

Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

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