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 complex nonlinear models.

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 demand forecasting using the publicly available Panama Short-Term Electricity Load Forecasting dataset. Gradient Boosting Regressor (GBR), XGBoost, LightGBM, CatBoost, and Random Forest are evaluated using 14 predefined walk-forward train–test splits that emulate the weekly forecasting protocol of Panama’s national grid operator. A common feature set consisting of lagged demand variables, a four-week moving average, temporal indicators, calendar variables, and Tocumen temperature is used for all models. A seasonal naive baseline, statistical significance testing, COVID-period split analysis, and feature importance comparison are also included. CatBoost achieved the best average performance with an RMSE of 55.52 MWh and MAPE of 3.80%, outperforming the seasonal naive baseline, which obtained an RMSE of 78.61 MWh. However, Wilcoxon-Holm testing showed that the narrow RMSE differences among the ensemble models were not statistically significant at the 5% level. Feature importance analysis confirmed that the four-week moving average is a dominant predictor for most models. The results show that ensemble tree-based models provide accurate, robust, and interpretable STLF performance under an operationally realistic evaluation protocol.

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

Accurate forecast of electricity demand has become essential for modern energy systems, which are Face-off escalating challenges because of industrial expansion, population growth, and the incorporation of renewable energy sources. With the development of machine learning methods, one can now efficiently forecast power consumption with the help of past data. This paper introduces a machine learning-based predictor of power consumption. We analyze various machine learning techniques, i.e., random forest, XGBoost, linear regression for power forecasting, in this work. These models were trained and tested on historical electricity consumption data from the Ministry of Electricity of Iraq, 2022 to 2025. The models' performance was evaluated using a number of metrics, such as Mean Absolute Error, Root Mean Squared Error, Mean absolute percentage error, and R-squared. the best performance model for demand was XGBoost, the model achieved on R-squared value of 0.98 and Linear regression model achieved on R-squared value of 0.98 for supply.

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 evolution of time series analysis methods for wind and photovoltaic (PV) power forecasting and establishes a comparative framework covering classical statistical models, intelligent learning algorithms, and hybrid modeling strategies. Based on two years of operational data collected from an actual wind farm and PV station in East China, the forecasting performance of ARIMA, exponential smoothing, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer architectures is comprehensively evaluated, while hybrid approaches based on Empirical Mode Decomposition (EMD) are further investigated. The results demonstrate that model selection should jointly consider forecasting horizon, data characteristics, and computational constraints. Classical statistical methods remain robust under stable operating conditions but are less effective in capturing extreme fluctuations, whereas deep learning approaches exhibit superior capability in modeling long-range temporal dependencies despite reduced interpretability. Decomposition-based hybrid strategies achieve a more balanced performance across diverse scenarios and show enhanced robustness under extreme weather conditions. The study further proposes a structured model selection guideline by matching data characteristics with operational requirements, providing theoretical support for forecasting system design in renewable-energy-driven power networks and offering useful references for electromagnetic energy utilization and intelligent industrial applications.

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.