What question did this study set out to answer?

To develop an accurate, data-driven residential electricity load forecasting framework to support scheduling and demand-side management in virtual power plants.

August 19, 2026Journal of Engineering and Applied ScienceOpen Access

Data-driven residential electricity load forecasting method for virtual power plants

View Full Paper

Ask AI

Bookmark

To develop an accurate, data-driven residential electricity load forecasting framework to support scheduling and demand-side management in virtual power plants.
Decomposed residential load time-series into trend, periodic, and residual components using seasonal-trend decomposition using loess (STL).
Applied a bidirectional gated recurrent unit (Bi-GRU) combined with an attention mechanism to capture bidirectional temporal dynamics and highlight critical load variations.
Achieved a forecasting performance of MAE 7.2, RMSE 11.85, and R² 0.88.
Reduced MAE by 16.28% and RMSE by 15.96% while improving R² by 6.02% compared with the baseline Bi-GRU model.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Modeling study demonstrates accurate residential electricity load forecasting using a hybrid deep learning framework, indicating enhanced day-ahead planning for virtual power plants.

Cite This Study

Hao et al. (2026) studied this question.

synapsesocial.com/papers/6a8563c403308d306e2d7267 https://doi.org/10.1186/s44147-026-01177-2

View Full Paper

Ask AI

Bookmark

Implication

Modeling study demonstrates accurate residential electricity load forecasting using a hybrid deep learning framework, indicating enhanced day-ahead planning for virtual power plants.

Data-driven residential electricity load forecasting method for virtual power plants

Key Points

Discussion

Implication

Cite This Study

Data-driven residential electricity load forecasting method for virtual power plants

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider