Data driven machine learning models for short-term load forecasting considering electrical vehicle load

Electric vehicles (EVs) are gaining popularity due to their fuel efficiency and ability to reduce greenhouse gas emissions. Significant penetration of EVs with unregulated charging, which can have a substantial impact on power networks. Accurate load predictions, including the charging of EVs are crucial for ensuring the cost-effective and dependable operation of power systems. In order to estimate short-term load in the presence of EV load, the XG (extreme gradient) boost algorithm is proposed and the effectiveness of the performances are checked against other models. A variety of distinct meteorological parameters and the electrical load pattern for the years 2017 and 2018 for Northeast India are used to train the machine learning classifier models. R-squared value analyses are also performed to identify the most correlated input parameters that influence the results of various models. Analysis shows that temperature, cloud cover, heat index, dew point, wind chill, and perceived temperature are substantially connected with electricity consumption. The performance of XG boost is outperformed by comparing prediction results with decision tree, random forest, K nearest neighbors, and logistic regression. Three separate case studies were employed for verification using the precision, F1 score, sensitivity, specificity, and accuracy metrics. Our findings demonstrate that, in comparison to other forecasting models, XG Boost exhibits higher accuracy (83.84%, 81.51%, and 85.97%) and robust forecasts.