Aug 2026· Al-imad Journal of Humanities and Applied Sciences· Vol 2, pp. 432-446· 0 citations
Abstract
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.
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
This study addresses short-term load forecasting (STLF) in the South, Southeast, and West Sulawesi (Sulselrabar) power system, Indonesia, using interval type-2 fuzzy logic (IT2FL) optimized by the proposed novel bat algorithm (NBA). The NBA is employed to optimize the footprint of uncertainty (FOU) of the fuzzy members...
Muhammad Rais, Rosihan Aminudin, Asnefi Asnefi et al.· International Journal of Adv...· 0 citations
The proposed short-term coupled forecasting model of power loads based on AI technology demonstrates high accuracy and robustness across different time scales and is helpful for the power industry to better cope with power market fluctuations and changes.
Li-Ling Peng, Tong Li, Guo-Feng Fan et al.· Computer Modeling in Enginee...· 0 citations
A comparative analysis of three machine learning models, namely Artificial Neural Network, Random Forest, and Support Vector Regression, for short-term solar PV power forecasting shows that Random Forest provides superior forecasting performance compared with ANN and SVR, achieving lower prediction errors and a higher...
Subash Ranjan Kabat, Priyadarshi Das, Rashmita Lenka et al.· International Research Journ...· 0 citations
Sustainable electrical network expansion, smart grid management, and effective power production all depend on accurate energy demand forecasts. Because machine learning (ML) approaches can simulate complicated nonlinear electrical load patterns and analyze historical data, they are frequently employed. In order to fore...
S. P. Samuel, S. Ramesh, V. G. Karthikeyan et al.· International Workshop on Ar...· 0 citations
In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the assessment tackles the comparison of data-driven models enhanced with other state-of-the-art techniques. At the first step, the existing techniques reported in the liter...
Nikolaos A. Efkarpidis, D. Kothona, Georgios C. Christoforidis· 0 citations
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