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

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, a...

Full description

Saved in:
Bibliographic Details
Published in:Future internet Vol. 15; no. 2; p. 46
Main Authors: Ramesh, Ganapathy, Logeshwaran, Jaganathan, Kiruthiga, Thangavel, Lloret, Jaime
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2023
Subjects:
Algorithms
Alternative energy sources
Coders
Construction
Deep learning
Distributed generation
Electricity
Electricity distribution
Energy consumption
Energy efficiency
energy generation
Energy management
Energy resources
Feature extraction
Force and energy
Green buildings
Image retrieval
Internet
Machine learning
Meteorological data
neural network
Neural networks
Photovoltaic cells
photovoltaic plants
prediction
Radiation
Radiation damage
Root-mean-square errors
Solar energy
Solar energy industry
Solar radiation
ISSN:1999-5903, 1999-5903
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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).
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1999-5903
1999-5903
DOI:10.3390/fi15020046