International Journal of Statistics, Econometrics and Data Analysis Applications

Short-term load forecasting using artificial neural network and linear regression across multi-city smart grid dataset

The experimental findings indicate that Linear Regression (LR) model is better than the Artificial Neural Network (ANN) model because it has a small Root Mean Square Error (RMSE), which means that the underlying data set is more linear in nature and in this case, simpler models can be more effective than the more compl...

A Comparative Study of Ensemble Tree-Based Models for Short-Term Electricity Load Forecasting

Short-term load forecasting (STLF) is an essential task for reliable power system operation, economic dispatch, reserve scheduling, and grid planning. This study aims to provide an operationally realistic and interpretable comparison of five ensemble tree-based machine learning (ML) models for national electricity dema...

Comparative Study of Machine Learning Techniques to Forecast Electricity Demand and Supply in Iraq

A machine learning-based predictor of power consumption, i.e., random forest, XGBoost, linear regression for power forecasting, is introduced and the best performance model for demand was XGBoost and the best performance model for supply was XGBoost.

Application and Comparative Study of Time Series Analysis Algorithms in New Energy Forecasting

The intermittent and volatile characteristics of new energy generation, together with the increasing demand for stable power supply in intelligent industrial systems, make accurate forecasting a critical issue for grid dispatch and electromagnetic energy management. This study systematically reviews the technological e...

International Journal of Statistics, Econometrics and Data Analysis Applications

Smart Forecasting of Power Consumption with Machine Learning Models

The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems, and supports the development of intelligent, sustainable, and data-driven energy.