// AI news · 2026-11-07 · Nature
Comparative analysis of long-term electricity load forecasting
A comparative study of long-term electricity load forecasting in Adigrat city uses historical economic and demographic data from 2016 to 2025.
Published 2026-11-07 08:00 (Beijing time)Source NatureAuto-summarised, fact-checked against source
01Summary
The research evaluates ML methods, LSTM, LSTM-CNN, and LSTM-GRU models, introducing a PSO-optimized LSTM-NN hybrid architecture with missing data imputation and feature selection. Results show superior accuracy and stability of the proposed model in long-term load prediction.
Summarised from the original article by an open-source model, then checked automatically: every number and English name must appear in the source. The original article is authoritative.
02Key points
- Uses historical economic and demographic data from 2016 to 2025 for 10-year load forecasting.
- Proposes PSO-optimized LSTM-NN hybrid model with missing data imputation.
- Introduces intelligent input combination feature selection for out-of-sample robustness.
- Demonstrates superior accuracy and stability of LSTM-NN-PSO in long-term prediction.
03Source
Nature · published 2026-11-07 08:00 Beijing time
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