Balancing the production and consumption of electricity is an urgent task. Its implementation
largely depends on the means and methods of planning electricity production. Forecasting is …

The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …

The use of solar energy has been rapidly expanding as a clean and renewable energy
source, with the installation of photovoltaic panels on homes, businesses, and large-scale …

Forecasting energy demand has been a critical process in various decision support systems
regarding consumption planning, distribution strategies, and energy policies. Traditionally …

Power system planning in numerous electric utilities merely relies on the conventional
statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is …

Predicting the future peak demand growth becomes increasingly important as more
consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts …

This study focuses on enhancing machine learning (ML) algorithms' performance in
predicting daily loads for Kirkuk, Iraq—an essential element in energy planning, resource …

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant
advantages for enhancing grid reliability and informing energy planning decisions …

Energy demand forecasting is crucial for planning and optimizing the use of energy
resources in building facilities. However, integrating digital solutions and learning …

Power has totally different attributes than other material commodities as electrical energy
stockpiling is a costly phenomenon. Since it should be generated when demanded, it is …