Jul 2026· International journal of pattern recognition and artificial intelligence· 0 citations
Abstract
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.
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.
Shorya Mittal, N. Saxena, K. Gandhi et al.· Journal of Electrical System...· 0 citations
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
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.
Short-term load forecasting (STLF) is serious to power system reliability, assistant economic operation, generation arrangement, and maintenance planning through precise demand estimates. This paper compares Multi-Parameter Regression (MPR) and Fuzzy Logic (FL) for guessing load on the Libyan grid. Using temperature, humidity, and old peak loads as input variables, the MPR model applies statistical regression to create a linear relationship, while the FL model uses fuzzy rules to report nonlinearities and uncertainty. Both models are trained on local data, with the FL system simulated via MATLAB/Simulink. The predicting performance of both techniques was evaluated and compared using standard error indices. The results validate the efficiency of each method in short-term load forecast and offer insights into their relative accuracy, flexibility, and suitability for use in the Libyan electric network.
Mahmoud Yousef Khamaira, Saleem Aqeel Altaeb, Ali Omar Alsharif et al.· Al-imad Journal of Humanitie...· 0 citations
Purpose of research.
With the increasing share of generation from solar plants in distribution networks, the requirements for the quality of supplied electricity are growing, as the instability of solar generation directly affects the network load and the voltage parameters of the electricity supplied to the consumer. The purpose of the research is experimental validation of methods for forecasting the output of a PV plant for the next day with hourly planning, which makes it possible to link the magnitude of the forecast error with the electric power quality indicators according to GOST 32144-2013.
Methods
. To solve this problem, a comparative study of three approaches was carried out: ARIMA/ARIMAX models, the Random Forest ensemble, and the hybrid STL+RF scheme. In all cases, the influence of meteorological factors on the accuracy of solar generation forecasting was analyzed.
Results
. It was found that the inclusion of weather data significantly improves the forecast quality compared to models using only the history of consumption. The best results were shown by the hybrid STL+RF model with meteorological features, which provided an optimal compromise between forecast accuracy and stability. This assessment shows that in the tasks of network operation management, the use of hybrid models is more justified than the use of statistical models alone.
Conclusion
. The proposed approach can be used as a basis for early warning systems for the risks of electric power quality deterioration in networks with a high share of RES. The practical value of the work lies in linking the results of solar generation forecasting with the regulatory requirements of GOST 32144-2013 and the tasks of dispatch control.
I. V. Brezhnev, I. Chernetskaya· Proceedings of the Southwest...· 0 citations
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