Energy Prediction and Optimization for Smart Homes with Weather Metric-Weight Coefficients

Area of Science:

• Smart Home Technology
• Energy Optimization
• Artificial Intelligence

Background:

• Home appliances, particularly Internet of Things (IoT) devices, significantly contribute to smart home energy consumption.
• Existing energy optimization techniques often neglect user convenience, a primary goal of smart home appliance integration.
• There's a need for advanced optimization methods that address the trade-off between energy efficiency and user comfort.

Purpose of the Study:

• To develop an optimization technique that effectively balances energy saving and user convenience in smart homes.
• To incorporate advanced weather metrics (air pressure, dew point, wind speed) beyond temperature and humidity for enhanced optimization.
• To enable proactive energy optimization through accurate appliance energy prediction.

Main Methods:

• A hybrid optimization approach combining Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) was modeled.
• An LSTM (Long Short-Term Memory) model was developed for predicting appliance energy consumption.
• The optimization technique was tested using simulations incorporating weather data and appliance energy predictions.

Main Results:

• The predictive LSTM model demonstrated low Root Mean Square Error (RMSE) values, indicating high prediction accuracy.
• Simulations confirmed that the proposed optimization strategy significantly reduces energy costs for controlling smart home appliances.
• Evaluations across seasonal and monthly data patterns verified the effectiveness of the energy cost reduction strategies.

Conclusions:

• The proposed hybrid GWO-PSO optimization technique effectively addresses the trade-off between energy saving and user convenience.
• Incorporating diverse weather metrics and predictive modeling enhances the proactive control of smart home appliances.
• This research presents a robust solution for optimizing smart home energy consumption, improving both efficiency and user experience.