The building sector takes a large proportion of electricity consumption and carbon emission in high-density urban areas. To reduce the carbon emissions and use energy more efficiently in the building sector, home energy management system (HEMS) is proposed and used. In the HEMS, the prediction of electricity consumption in the short-term future is used to support the decision makings in the HEMS. Although there existed a number of studies in the prediction of electricity consumption in buildings, there lacks a spatial analysis in the prediction performance, especially on the appliance or sub-meter level and household level. The authors made an assumption that by the performance of household energy consumption prediction can be significantly improved if the prediction is aggregated from the prediction data at the appliance or sub-meter level. Next, two typical datasets are used to validate the assumption by comparing the prediction performance of aggregating the prediction data at appliance level and the one of making direct prediction at the household level. The models used for the prediction are standard stateful long short-term memory (LSTM) neural networks, which have been proofed to be promising in load prediction by previous studies. The results from the comparison validated the assumption, showing that the prediction performance can be significantly improved if prediction is made at the appliance-level first and then aggregated to get the household-level prediction. Therefore, the authors conclude that prediction at the finer appliance granularity level can significantly improve the performance of household-level electricity prediction.

electricity consumption prediction; LSTM model; appliance level; submeter level; household level; spatial granularity analysis


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