Enhancing Solar Energy Production Forecasting Using Advanced Machine Learning and Deep Learning Techniques: A Comprehensive Study on the Impact of Meteorological Data

作     者：Nataliya Shakhovska Mykola Medykovskyi Oleksandr Gurbych Mykhailo Mamchur Mykhailo Melnyk 

作者机构：Instutute of Computer Science and Information TechnologiesLviv Polytechnic National UniversityLviv79013Ukraine Department of Applied MathematicsUniversity of Agriculture in KrakowKrakow31-120Poland Blackthorn AILtd.LondonEC1V 2NXUK 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第11期

页      面：3147-3163页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：funded by the H2020 Project ZEBAI 

主　　题：Solar energy prediction machine learning deep learning 

摘      要：The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability,reliability,and economic *** study explores advanced machine learning(ML)and deep learning(DL)techniques for predicting solar energy generation,emphasizing the significant impact of meteorological data.A comprehensive dataset,encompassing detailed weather conditions and solar energy metrics,was collected and preprocessed to improve model *** models were developed and trained with different preprocessing ***,three datasets were prepared.A novel hour-based prediction wrapper was introduced,utilizing external sunrise and sunset data to restrict predictions to daylight hours,thereby enhancing model performance.A cascaded stacking model incorporating association rules,weak predictors,and a modified stacking aggregation procedure was proposed,demonstrating enhanced generalization and reduced prediction *** indicated that models trained on raw data generally performed better than those on stripped *** Long Short-Term Memory(LSTM)with Inception layers’model was the most effective,achieving significant performance improvements through feature selection,data preprocessing,and innovative modeling *** study underscores the potential to combine detailed meteorological data with advanced ML and DL methods to improve the accuracy of solar energy forecasting,thereby optimizing energy management and planning.