Data-Driven Load Forecasting Using Machine Learning and Meteorological Data  

作　　者：Aishah Alrashidi Ali Mustafa Qamar 

机构地区：[1]Department of Computer Science,College of Computer,Qassim University,Buraydah,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第3期1973-1988,共16页计算机系统科学与工程（英文）

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

摘　　要：Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both 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 forecasting.The 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 Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After 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 load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

关 键 词：Electricity load forecasting meteorological data machine learning feature selection modeling real-world problems predictive analytics 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]