Prediction of Energy Production Level in Large PV Plants through AUTO-Encoder Based Neural-Network (AUTO-NN) with Restricted Boltzmann Feature Extraction

Ganapathy Ramesh, Jaganathan Logeshwaran, Thangavel Kiruthiga and Jaime Lloret ()
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Ganapathy Ramesh: Department of Information Technology, KLN College of Engineering, Madurai 630612, India
Jaganathan Logeshwaran: Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, India
Thangavel Kiruthiga: Department of Electronics and Communication Engineering, Vetri Vinayaha College of Engineering and Technology, Thottiam 621214, India
Jaime Lloret: Integrated Management Coastal Research Institute, Universitat Politecnica de Valencia, Camino Vera s/n, 46022 Valencia, Spain

Future Internet, 2023, vol. 15, issue 2, 1-20

Abstract: In general, reliable PV generation prediction is required to increase complete control quality and avoid potential damage. Accurate forecasting of direct solar radiation trends in PV power production could limit the influence of uncertainties on photovoltaics, enhance organizational dependability, and maximize the utilization factor of the PV systems for something such as an energy management system (EMS) of microgrids. This paper proposes an intelligent prediction of energy production level in large PV plants through AUTO-encoder-based Neural-Network (AUTO-NN) with Restricted Boltzmann feature extraction. Here, the solar energy output may be projected using prior sun illumination and meteorological data. The feature selection and prediction modules use an AUTO encoder-based Neural Network to improve the process of energy prediction (AUTO-NN). Restricted Boltzmann Machines (RBM) can be used during a set of regulations for development-based feature extraction. The proposed model’s result is evaluated using various constraints. As a result, the proposed AUTO-NN achieved 58.72% of RMSE (Root Mean Square Error), 62.72% of nRMSE (Normalized Root Mean Square Error), 48.04% of MaxAE (Maximum Absolute Error), 48.66% of (Mean Absolute Error), and 46.76% of (Mean Absolute Percentage Error).

Keywords: energy generation; prediction; photovoltaic plants; feature extraction; neural network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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