Prediction of electrical load demand using combined LHS with ANFIS

• Published in: PloS one Vol. 20; no. 6; p. e0325747
• Main Authors: Ismail, Ahmed G., Elbanna, Sayed H. A., Mohamed, Hassan S.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.06.2025; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptability; Adaptive systems; Algorithms; Biology and Life Sciences; Comparative analysis; Computer and Information Sciences; Datasets; Decision making; Demand analysis; Effectiveness; Efficiency; Egypt; Electric power demand; Electric Power Supplies; Electric power systems; Electrical loads; Electricity; Energy consumption; Energy demand; Energy management; Engineering and Technology; Forecasting; Fuzzy Logic; Health facilities; Humans; Hypercubes; Infrastructure; Latin hypercube sampling; Literature reviews; Machine Learning; Medicine and Health Sciences; Methods; Monte Carlo Method; Monte Carlo simulation; Neural networks; Neural Networks, Computer; Performance measurement; Physical Sciences; Predictions; Research and Analysis Methods; Resource allocation; Risk assessment; Sampling; Sensitivity analysis; Social Sciences; Stratified sampling; Support vector machines; Time series; Egypt
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0325747

Enhancement prediction of load demand is crucial for effective energy management and resource allocation in modern power systems and especially in medical segment. Proposed method leverages strengths of ANFIS in learning complex nonlinear relationships inherent in load demand data. To evaluate the effectiveness of the proposed approach, researchers conducted hybrid methodology combine LHS with ANFIS, using actual load demand readings. Comparative analysis investigates performing various machine learning models, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS) alone, and ANFIS combined with Latin Hypercube sampling (LHS), in predicting electrical load demand. The paper explores enhancing ANFIS through LHS compared with Monte Carlo (MC) method to improve predictive accuracy. It involves simulating energy demand patterns over 1000 iterations, using performance metrics through Mean Squared Error (MSE). The study shows superior predictive performance of ANFIS-LHS model, achieving higher accuracy and robustness in load demand prediction across different time horizons and scenarios. Thus, findings of this research contribute to advanced developments rather than previous research by introducing a combined predictive methodology that leverages LHS to ensure solving limitations of previous methods like structured, stratified sampling of input variables, reducing overfitting and enhancing adaptability to varying data sizes. Additionally, it incorporates sensitivity analysis and risk assessment, significantly improving predictive accuracy. Using Python and Simulink Matlab, Combined LHS with ANFIS showing accuracy of 96.42% improvement over the ANFIS model alone.