Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

作     者：Aishah Alrashidi Ali Mustafa Qamar 

作者机构：Department of Computer ScienceCollege of ComputerQassim UniversityBuraydahSaudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第3期

页      面：1973-1988页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funding Statement:The researchers would like to thank the Deanship of Scientific Research Qassim University for funding the publication of this project 

主　　题：Electricity load forecasting meteorological data machine learning feature selection modeling real-world problems predictive analytics 

摘      要：Electrical load forecasting is very crucial for electrical power systems’planning and *** electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load *** meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi *** factory load and meteorological data used in this study are recorded hourly between 2016 and *** data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in *** applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory *** addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this *** outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning ***,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.