Evaluating the Significance of Financial Characteristics on Energy Consumption of Urban Building Stock using Principal Component Analysis and Logistic Regression
Vishnu Sivarudran Pillai1, Arathi KV2
1Vishnu Sivarudran Pillai, Department of Public Policy, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
2Arathi KV, Ezra Homes, Kochi (Kerala), India.
Manuscript received on 30 June 2020 | Revised Manuscript received on 07 July 2020 | Manuscript Published on 11 August 2020 | PP: 17-25 | Volume-9 Issue-9S July 2020 | Retrieval Number: 100.1/ijitee.I10040799S20| DOI: 10.35940/ijitee.I1004.0799S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The increased population and the rapid urbanization seek our attention towards sustainable production and consumption in cities. In assessing the factors affecting the energy consumption characteristics of the buildings, it is crucial that we consider the user behavior along with the design characteristics of the buildings. One significant factor that influence the user behavior is the financial characteristics. We use non-parametric machine learning algorithms and econometric models to assess the influence of the user behavior characteristics in the urban building stock in New York City. The analysis was conducted on the open-data assessable, which is mandated by the Local Law 84. In our analysis we concluded that the financial characteristics have a significant effect in the energy consumption of the residential buildings, however, is not that significant in deciding the energy consumption of the commercial buildings.
Keywords: Building Energy, Principal Component Analysis, Logistic Regression, Energy Usage Intensity.
Scope of the Article: Building Energy