June 30, 2025AI EngineeringOpen Access

Building Electricity Load Forecasting Considering Climate Change Impacts: A Multi-Factor Deep Learning Approach

View Full Paper

Ask AI

Bookmark

Authors

YWYuqing Wang QLQIAOQIAO LI YXYan Xu

Discussion

Member takes

Implication

Comparative analysis shows deep neural network models improve electricity load forecasting, suggesting climate change impacts energy demand in urban environments.

Key Points

Electricity load forecasting improves using deep neural network models considering temperature conditions.
The best performance observed in models incorporating climate change impacts on energy demand.
Analysis of electrical load data from Singapore from 2019 to 2022 was undertaken.
Integrating climate factors enhances prediction accuracy, highlighting needs in urban energy planning.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/69255726c0ce034ddc35a424 https://doi.org/10.53941/aieng.2025.100003

View Full Paper

Ask AI

Bookmark

Implication

Comparative analysis shows deep neural network models improve electricity load forecasting, suggesting climate change impacts energy demand in urban environments.

Building Electricity Load Forecasting Considering Climate Change Impacts: A Multi-Factor Deep Learning Approach

Discussion

Member takes

Implication

Key Points

Cite This Study

Building Electricity Load Forecasting Considering Climate Change Impacts: A Multi-Factor Deep Learning Approach

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider