Three machine learning models, standalone XGBoost, Long Short-Term Memory (LSTM), and a hybrid model combining LSTM and XGBoost were systematically compared to two naive models: persistence and seasonal benchmark models to illustrate the need for selecting the most appropriate model based on data to accurately predict energy consumption in smart homes.
Three machine learning models, standalone XGBoost, Long Short-Term Memory (LSTM), and a hybrid model combining LSTM and XGBoost were systematically compared to two naive models: persistence and seasonal benchmark models to illustrate the need for selecting the most appropriate model based on data to accurately predict...
Rand Jalal, I. Al-Mejibli· Iraqi Journal for Computers...· 0 citations
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...
Timur Lale· 2026 6th International Confe...· 0 citations
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...
Shorya Mittal, N. Saxena, K. Gandhi et al.· Journal of Electrical System...· 0 citations
The forecasting of short-term load (STLF) is an important part of the functioning of a power system, as it aids in discovering the dispatch plans and taking the strain off. This paper researches hourly short-term electricity load forecasting of the Panama power system based on a univariate Autoregressive Integrated Mov...
An integrated probabilistic forecasting framework with three linked stages: extreme-weather load identification, TimeGAN-based sample augmentation, and conformal quantile forecasting, which improves forecasting accuracy under extreme-weather conditions.
Hao Zhang, Xi-Yang Liu, Ruotian Gao et al.· International journal of pat...· 0 citations
This study presents a next-day electricity load forecasting for Tirana, Albania, by combining temperature-based indicators, Cooling Degree Days and Heating Degree Days in a multi- input-output modeling framework, showing that forecasting performance is season-dependent, with different models achieving the highest accur...
Agresa Qosja· European Journal of Energy R...· 0 citations
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