Prediction of electrical load demand using combined LHS with ANFIS
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| Publicado en: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0325747 |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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|---|---|---|---|
| 001 | 3217665448 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0325747 |2 doi | |
| 035 | |a 3217665448 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Ismail, Ahmed G | |
| 245 | 1 | |a Prediction of electrical load demand using combined LHS with ANFIS | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Resource allocation | ||
| 653 | |a Energy management | ||
| 653 | |a Accuracy | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Electrical loads | ||
| 653 | |a Datasets | ||
| 653 | |a Adaptability | ||
| 653 | |a Forecasting | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Demand analysis | ||
| 653 | |a Hypercubes | ||
| 653 | |a Health facilities | ||
| 653 | |a Sampling | ||
| 653 | |a Risk assessment | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Energy demand | ||
| 653 | |a Energy consumption | ||
| 653 | |a Efficiency | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Performance measurement | ||
| 653 | |a Infrastructure | ||
| 653 | |a Predictions | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Effectiveness | ||
| 653 | |a Electric power demand | ||
| 653 | |a Methods | ||
| 653 | |a Literature reviews | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Latin hypercube sampling | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Elbanna, Sayed H A | |
| 700 | 1 | |a Mohamed, Hassan S | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0325747 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3217665448/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3217665448/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3217665448/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |