Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction DOI Open Access
Sicheng Wan, Yibo Wang,

Youshuang Zhang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 6903 - 6903

Published: Aug. 12, 2024

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because crude modules for predicting short-term medium-term loads. To solve such a problem, Combined Modeling Power Load-Forecasting (CMPLF) method proposed in this work. The CMPLF comprises two deal with forecasting, respectively. Each module consists four essential parts including initial decomposition denoising, nonlinear optimization, evaluation. Especially, break through bottlenecks hierarchical model we effectively fuse Nonlinear Autoregressive Exogenous Inputs (NARX) Long-Short Term Memory (LSTM) networks into Integrated Moving Average (ARIMA) model. experiment results based on real-world datasets Queensland China mainland show that our has significant performance superiority compared state-of-the-art (SOTA) methods. achieves goodness-of-fit value 97.174% 97.162% prediction. Our approach will be great significance promoting sustainable development smart cities.

Language: Английский

Short-term forecasting of rooftop retrofitted photovoltaic power generation using machine learning DOI
Mohd Herwan Sulaiman, Mohd Shawal Jadin, Zuriani Mustaffa

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 94, P. 109948 - 109948

Published: June 14, 2024

Language: Английский

Citations

5
Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction DOI Open Access
Sicheng Wan, Yibo Wang,

Youshuang Zhang

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(16), P. 6903 - 6903

Published: Aug. 12, 2024

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because crude modules for predicting short-term medium-term loads. To solve such a problem, Combined Modeling Power Load-Forecasting (CMPLF) method proposed in this work. The CMPLF comprises two deal with forecasting, respectively. Each module consists four essential parts including initial decomposition denoising, nonlinear optimization, evaluation. Especially, break through bottlenecks hierarchical model we effectively fuse Nonlinear Autoregressive Exogenous Inputs (NARX) Long-Short Term Memory (LSTM) networks into Integrated Moving Average (ARIMA) model. experiment results based on real-world datasets Queensland China mainland show that our has significant performance superiority compared state-of-the-art (SOTA) methods. achieves goodness-of-fit value 97.174% 97.162% prediction. Our approach will be great significance promoting sustainable development smart cities.

Language: Английский

Citations

0