Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition  

作　　者：Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek 

机构地区：[1]LMTESE Laboratory of Materials,Technology of Energy Systems and Environment,Universitéde Ghardaia,47000,Algeria [2]Renewable Energy Applied Research Unit(URAER),Ghardaia,47000,Algeria [3]Laboratory of Mathematics Modeling and Applications,Department of Mathematics and Computer Science,Faculty of Sciences and Technology,Ahmed Draia University of Adrar,Adrar 01000,Algeria

出　　处：《Clean Energy》2024年第3期95-108,共14页清洁能源（英文）

摘　　要：Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power stations.This study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead.The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models.Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all seasons.The proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction horizons.However,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results.This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关 键 词：short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy 

分 类 号：TK51[动力工程及工程热物理—热能工程]