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Bio-inspired puma optimization algorithm for time-series energy consumption forecasting
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DOI：10.1016/j.egyr.2025.109022.png)
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Abstract
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• Hybrid PO-LSTM model for smart home energy time-series forecasting. • bPSO-Guided WOA feature selection reduces complexity and boosts accuracy. • PO-optimized LSTM achieves RMSE 0.00582 and R² 0.98060 on residential data. • Outperforms GRU, RNN, ANN and other metaheuristic-tuned LSTM models. • Supports demand-side management and smart grid residential applications.
Keywords:
Bio-Inspired Optimization
Puma Optimizer
LSTM Networks
Smart Home Energy Forecasting
Feature Selection
Time-Series Prediction
Metaheuristic Algorithms
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