Smart homes are becoming increasingly popular as they offer many benefits to their occupants, such as remote monitoring and control of various functions. However, such homes consume a lot of energy, leading to high energy bills and environmental concerns. In this context, optimising energy consumption in smart homes has become an area of research.
Predicting and Optimising Energy Consumption in Smart Homes
In a study by Korean scientists, a method is proposed for predicting and optimising energy consumption in smart homes using weather metric-weight coefficients.
The proposed method involves analysing historical data on energy consumption and weather patterns to calculate weather metric-weight coefficients. These coefficients are then used to predict energy consumption based on the current and expected weather conditions. Using these coefficients, intelligent homes can adjust their energy consumption based on the predicted weather conditions, ensuring that the house is always energy efficient.
Validation of the Proposed Method
The scientists conducted experiments to validate the proposed method, and the results showed that it could accurately predict energy consumption with an average error rate of 6.12%. The high accuracy rate of the proposed method is attributed to the use of weather metric-weight coefficients, which consider the effect of weather conditions on energy consumption.
Optimisation of Energy Consumption
Furthermore, the scientists used the predicted energy consumption values to optimise energy consumption by adjusting the setpoints of various devices in the smart home, such as the thermostat and lighting. The optimisation process is performed by a genetic algorithm, which finds the optimal setpoints that minimise energy consumption while maintaining the desired level of comfort.
Promising Results and Applications
The proposed energy consumption prediction and optimisation method in smart homes using weather metric-weight coefficients have shown promising results. The method can reduce energy costs and conserve the environment by ensuring that smart homes are always energy efficient. The method is promising and can be applied to many smart homes, making it a valuable tool for building energy management.
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