A Data-Driven Approach to Energy Cost Prediction Of Dwellings Based on Partitioned Parallel Modeling

In today's modern society, residential dwellings make up a large proportion of the world's energy consumption. Therefore, predicting dwellings' energy consumption and needs are highly useful for energy conservation and evaluating future demand. Data-driven energy consumption prediction models are widely researched to forecast energy consumption of buildings. However, in the majority of studies, the training data is collected through smart energy meters and sensors of some target sample buildings. Thus, the source of data intrinsically restricts the usable range of prediction models. In this paper, we build a set of innovative partitioned parallel prediction models include Support Vector Regression (SVR) and Random Forest Regression (RFR) to predict annual heating costs, hot water costs and lighting costs of dwellings. The model training and valuation procedures are basing on the data of the Standard Assessment Procedure and Energy Performance Certificate (SAP-EPC), covering all 13 main regions in the United Kingdom. The feature selection comparative experiments prove that the merge feature selector can select valid feature sets according to different prediction targets The comparative experiment results manifest the fact that the parallel modeling approach can improve the model's accuracy. Meanwhile, the experimental results indicate that the proposed prediction models can accurately forecast the energy consumption of the dwellings.