Adaptive Machine Learning for Automatic Load Optimization in Connected Smart Green Townhouses

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 132 - 132

Published: March 2, 2025

This paper presents an adaptive Machine Learning (ML)-based framework for automatic load optimization in Connected Smart Green Townhouses (CSGTs) The system dynamically optimizes consumption and transitions between grid-connected island modes. Automatic mode reduce the need manual changes, ensuring reliable operation. Actual occupancy, demand, weather, energy price data are used to manage loads which improves efficiency, cost savings, sustainability. An is employed that combines processing ML. A hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) model analyze time series spatial data. Multi-Objective Particle Swarm Optimization (MOPSO) balance costs, carbon emissions, efficiency. results obtained show a 3–5% improvement efficiency 10–12% mode, as well 4–6% reduction emissions.

Language: Английский

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Sustainable mobility in India: advancing domestic production in the electric vehicle sector

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 23, 2025

Language: Английский

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Solar and wind predictions using evolution differentiated modular and LSTM long-term modelling based on pattern correlations

Research in Mathematics, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 27, 2025

Language: Английский

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