Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode DOI Creative Commons
Seyed Morteza Moghimi, T. Aaron Gulliver,

T. Ilamparithi

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6475 - 6475

Published: Dec. 23, 2024

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, meters, high-efficiency systems. These elements improve energy efficiency promote sustainability. Operating island mode, CSGBs can function independently of the grid, providing resilience during power outages reducing reliance external sources. Real data electricity, gas, water consumption are used optimize load management under isolated conditions. Electric Vehicles (EVs) also considered system. They serve storage devices and, through Vehicle-to-Grid (V2G) technology, supply when needed. A hybrid Machine Learning (ML) combining Long Short-Term Memory (LSTM) Convolutional Neural Network (CNN) is performance. metrics include accuracy, efficiency, emissions, cost. performance was compared several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), LSTM–CNN, results show that provides best results. For four-bedroom Townhouse (CSGT), Mean Absolute Percentage Error (MAPE) 4.43%, Root Square (RMSE) 3.49 kWh, (MAE) 3.06 R2 0.81. indicate robust optimization, particularly highlight potential for urban living.

Language: Английский

Adaptive Machine Learning for Automatic Load Optimization in Connected Smart Green Townhouses DOI Creative Commons
Seyed Morteza Moghimi, T. Aaron Gulliver,

T. Ilamparithi

et al.

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

Citations

0
Energy Efficiency and the Transition to Renewables—Building Communities of the Future DOI Creative Commons
Efstathios E. Michaelides

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1778 - 1778

Published: April 2, 2025

The effects of energy efficiency on the decarbonization engineering infrastructure were examined by simulating hourly demand a small Texan city with 10,000 buildings. available renewable sources in region, wind and solar, supply required energy, deficit or surplus is offset storage. demand–supply match during every hour year determines power, storage requirement, dissipation storage/regeneration processes. computations showed that implementation measures will decrease total power factor 2.9, needed 2.0, annual 2.4. Of particular interest determination transition elasticity coefficients, which offer quantitative interpretation better understanding efforts communities.

Language: Английский

Citations

0
Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode DOI Creative Commons
Seyed Morteza Moghimi, T. Aaron Gulliver,

T. Ilamparithi

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6475 - 6475

Published: Dec. 23, 2024

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, meters, high-efficiency systems. These elements improve energy efficiency promote sustainability. Operating island mode, CSGBs can function independently of the grid, providing resilience during power outages reducing reliance external sources. Real data electricity, gas, water consumption are used optimize load management under isolated conditions. Electric Vehicles (EVs) also considered system. They serve storage devices and, through Vehicle-to-Grid (V2G) technology, supply when needed. A hybrid Machine Learning (ML) combining Long Short-Term Memory (LSTM) Convolutional Neural Network (CNN) is performance. metrics include accuracy, efficiency, emissions, cost. performance was compared several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), LSTM–CNN, results show that provides best results. For four-bedroom Townhouse (CSGT), Mean Absolute Percentage Error (MAPE) 4.43%, Root Square (RMSE) 3.49 kWh, (MAE) 3.06 R2 0.81. indicate robust optimization, particularly highlight potential for urban living.

Language: Английский

Citations

1