Aug 2026· Journal of Electrical Systems and Information Technology· Vol 13· 0 citations· 18 references
TL;DR
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.
Abstract
Smart grid activities demand a remedy to stability and economic problems and Short-term load forecasting (STLF) can provide some of the basic methods to deal with them. The traditional forecasting models that are found in literature flounder about localised and region specific data sets and therefore, their acceptability of results in general is always a demanding task without their strict cross validation. This current paper develops a distinctive solution with a multi city dataset that is extensive to determine the predictive quality of Artificial Neural Networks (ANN) and Linear Regression (LR) models. The test is carried out based on the high-resolution data on hourly data on NASA Earth data platform (n = 48,048) on January 2015 to June 2020, which includes national level data on electricity demand in Panama on the National Dispatch Center (CND) and local weather data. The proposed analytical framework has incorporated two-meter elevation variables such as temperature, humidity, wind speed and precipitation in three strategic region hubs of United States viz. Tocomen, Santiago and David. The model also takes into account exogenous temporal features, including the public holidays and the academic calendars to test the change in the socio-economic loads. 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). This 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, where Linear Regression performs better than ANN in the current circumstances of the data set.
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.
Timur Lale· 2026 6th International Confe...· 0 citations
The empirical results indicate that tree-based ensemble models outperform deep learning approaches under the considered dataset conditions and suggest that ensemble machine learning techniques are more suitable for data-constrained environments, where deep learning models may suffer from overfitting.
Lamiaa Fares, Moad El Kharrim, Mohamed Dakkon· 0 citations
The effects of long heat waves are cumulative and the electricity demand is highly non-stationary in its nature. In order to solve this issue, a multi-stage prediction system has been suggested in this paper with a focus on the extended high-temperature conditions. To begin with, scenario subsets were built by classifying historical daily load curves based on their similarity in shape. Second, fuzzy inference was used to incorporate temperature of forecast day, number of consecutive days of high temperatures and accumulated intensity of heat to create an equivalent load-response temperature feature. Third, the frequency sequence of the load is divided into two parts of high-frequency and low-frequency as well as the noise and mixing of modes are minimized. The final model of the components is then predicted separately and combined to form the resulting load prediction. The proposed framework can be evaluated using 15-minute load and meteorological data, and the results indicate that it has less MAPE, MAE, and RMSE than the comparison models and also follows the peak and rapid changes in the load during the period of prolonged heatwaves. The findings show that multiscale forecasting, load-scenario classification, and cumulative heat response representation may enhance the accuracy and strength of short-term load forecasts under the condition of sustained high-temperature levels.
Xi-Yang Liu, Hao Zhang, Mengtao Sun et al.· International journal of pat...· 0 citations
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.
Accurate short-term forecasting of electricity loads is crucial for power system operations in regions with large fluctuations in daily energy demand caused by climate variability. 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. The forecasting of daily electricity load and degree day variables was carried out using several forecasting methods, including Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting and Artificial Neural Networks, over training and testing scenarios based on the daily data collected from 2020 to 2022. The forecasting framework includes short-term temporal and temperature-dependent dynamics using lagged electricity demand and degree-day indicators. The model performance was evaluated under three different seasonal scenarios: August (high cooling demand), October (neutral transition conditions), and November (high heating demand). The results indicate that under neutral conditions, all models exhibit similar performance, with an average MAPE of approximately 2.7% in October. Random Forest has the best load forecasting accuracy in August (MAPE= 4.07%), and Support Vector Regression has the best load forecasting accuracy in November (MAPE= 3.27%), followed by the tree- based ensemble methods. The results show that forecasting performance is season-dependent, with different models achieving the highest accuracy under different climatic conditions. Rather than identifying a single globally optimal model, the findings highlight the importance of regime-specific model behavior in electricity demand forecasting. This suggests that model selection should be aligned with seasonal characteristics of the load, particularly in temperature-sensitive systems. The findings provide useful information for short-term operational planning in the Albanian electricity sector.
Agresa Qosja· European Journal of Energy R...· 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 Moving Average (ARIMA) model as a clean statistical reference. The national demand in 20152020 was chosen as the dataset obtained in the publicly available Kaggle repository. It was tested on a 24-hour-ahead forecasting problem with a train-test split that was based on chronology. The ARIMA (2,1,2) model gained the reliability of the held-out test window of 48.88 MWh, RMSE of 60.50 MWh, and MAPE of 4.32%. Although this error rate is widely similar to classical statistical thresholds in short-horizon load forecasting papers, it is nevertheless significantly larger than the accuracy commonly found by the more advanced machine learning and deep learning models, specifically when using exogenous variables. The results hence result in ARIMA being an efficient base of clarity and benchmarking, with the development being more expressive models to route the non-linearities in electricity demand.
Zexin Wang· ITM Web of Conferences· 0 citations
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