Preprint Article Version 1 This version is not peer-reviewed

Energy Management through Cost Forecasting for Residential Buildings in New Zealand

Version 1 : Received: 10 June 2019 / Approved: 12 June 2019 / Online: 12 June 2019 (03:40:57 CEST)

How to cite: Zhao, L.; Liu, Z.; Mbachu, J. Energy Management through Cost Forecasting for Residential Buildings in New Zealand. Preprints 2019, 2019060099 (doi: 10.20944/preprints201906.0099.v1). Zhao, L.; Liu, Z.; Mbachu, J. Energy Management through Cost Forecasting for Residential Buildings in New Zealand. Preprints 2019, 2019060099 (doi: 10.20944/preprints201906.0099.v1).

Abstract

Over the last two decades, residential buildings have accounted for nearly 50 percent of total energy use in New Zealand. In order to reduce household energy use, the factors that influence energy use should be continuously monitored and managed. Building researchers and professionals have made efforts to investigate the factors that affect energy use. However, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the correlation between energy use data and residential building cost indicated that residential building cost in the construction phase and energy use in the operation stage were significantly correlated. These findings suggest that correct monitoring of building costs can help to identify trends in energy use. Therefore, this study proposes a time series model for forecasting residential building costs of five categories of residential building (one-story house, two-story house, townhouse, apartment, retirement village) in New Zealand. The primary contribution of this paper is the identification of the close correlation between household energy use and residential building costs and provide a new area for optimize energy management.

Subject Areas

residential energy use; energy management; residential building costs; exponential smoothing method; ARIMA model

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