Work

Estimation of Daily Photovoltaic Power One Day Ahead With Hybrid Deep Learning and Machine Learning Models

Year: 2025

Type: article

Abstract: ABSTRACT In this study, hybrid LSTM‐SVM and hybrid LSTM‐KNN models were developed to predict hourly PV power one day ahead. The performances of these hybrid models were compared with K‐nearest neighbors (KNN), long short‐term memory (LSTM), and support vector machine (SVM) models. The input data of these models were pressure, cloudiness, humidity, temperature, and solar intensity, while the output data was the daily photovoltaic (PV) power one day ahead. The performances of the models were evaluated using mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), and peak signal‐to‐noise ratio (PSNR). The prediction accuracies of hybrid LSTM‐KNN, LSTM, KNN, hybrid LSTM‐SVM, and SVM were 98.72%, 95.8%, 90.25%, 76.3%, and 48.87%, respectively. Hybrid LSTM‐KNN predicted the daily PV power of the day ahead with higher accuracy than LSTM, KNN, SVM, and hybrid LSTM‐SVM. The effect of input variables on output variables was examined with sensitivity analysis. Sensitivity analyses showed that the most important meteorological data affecting the daily PV power one day ahead was solar intensity with a rate of 95%.

Source: Energy Science & Engineering

DOI: 10.1002/ese3.1994

Author: Tuba T. Ağır

Institution: Batman University

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,780