Urban electricity–temperature relationships in the Tokyo Metropolitan Area

• Published in: Energy and buildings Vol. 256; p. 111729
• Main Authors: Nakajima, Ko, Takane, Yuya, Fukuba, Shinya, Yamaguchi, Kazuki, Kikegawa, Yukihiro
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 01-02-2022; Elsevier BV
• Subjects: Air conditioning; Air temperature; Building energy; Climate models; Electric power; Electric power generation; Electric utilities; Electrical transmission; Electricity; Electricity consumption; Electricity distribution; Energy management systems; Metropolitan areas; Residential areas; Scattering; Sensitivity of electricity consumption; Spatial variations; Substations; Urban energy; Sensitivity of electricity consumption; Air conditioning; Urban energy; Electricity consumption; Building energy
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2021.111729

•We collected high-resolution (a few km2) electricity consumption (EC) data.•EC sensitivity to temperature (ΔEC/ΔT) was classified by building use.•ΔEC/ΔT variation was larger in the business area than residential area.•The ΔEC/ΔT in the business area was inversely proportional to building size.•Our work will improve energy management systems and urban climate modelling.
The electricity consumption (EC)–outdoor near-surface air temperature (T) relationships in the Tokyo Metropolitan Area were revealed by EC data at 1290 substations with high temporal and spatial resolutions provided by an electric power company. Temporal–spatial variation in the sensitivity of EC to T (ΔEC/ΔT) was seen. The daytime ΔEC/ΔT values in business and commercial areas were larger than in residential areas. The ΔEC/ΔT values in business substations showed larger scatter than those in residential substations. One source of this scatter was building size, which affects the constitution of AC systems installed in the buildings. This study is the first to describe such detailed ΔEC/ΔT data (including uncertainties). The work will aid development of efficient energy management systems and the results can be used as a benchmark and input data to improve urban climate and building energy models.