A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System

IEEE Access

Santiago Banales Lopez¹  Mahdi Houchati¹  Haitham Abu-Rub²  Dabeeruddin Syed²  Ali Ghrayeb³  Ameema Zainab³  Shady S. Refaat³  Othmane Bouhali³ 

[1] M University at Qatar, Doha, Qatar;M University, College Station, TX, USA;Texas A&x0026;

关键词: Big data applications;    machine learning algorithms;    parallel processing;    load forecast;    smart grids;   

DOI  :  10.1109/ACCESS.2021.3059730

来源: DOAJ

【 摘 要 】

For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers' level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (~24 million records) utilizing 32 processors.

【 授权许可】

Unknown