In recent years, municipalities have started to develop their own energy plan. Therefore, a quantitative evaluation approach considering energy demand distribution is of benefit. In foreign countries, energy maps which visualize the demand distribution have been used to make an effective decision regarding energy planning. Over the past few years, several studies have analyzed using energy maps, but this approach has not yet been conducted on rural areas which are rich in energy resources. This paper presents the method for making energy map for rural town and applying energy map for designing a biomass district heating system considering pipe network heat loss and pumping power demand based on heat demand distribution. The case study was done for Mogami-cho, Japan. The results indicate the case of DHS connected with both high demand commercial buildings and single detached houses is economically competitive. And, the introduction of biomass based district heating system is recommended in the area with linear heat density of 4.2 GJ/m.

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This paper presents an estimation model of residential energy demand, based on questionnaire surveys. The questionnaire surveys, which were performed in 2014 winter and summer, asked over 1200 people, male over 20 years old and housewives over 30 years old, about their life activities on a day, (1) where you were, (2) what you were doing there, and (3) whether you were using home appliances and hot water, in every 15 minutes.
In this estimation model, family life activities are made from the questionnaire results to estimate their electricity and hot water load profiles. The questionnaire focuses on life activity relationship between family members and actual home appliances and hot water use. With this model, we estimated 420 patterns of electricity and hot water demand profiles of various seasons, family structures and lifestyles. The result suggests that family structure influences the shape of energy demand profile and daily energy consumption. Estimated energy demands are similar to the secondary energy consumption in residential sector. We also compare the electricity and hot water demand profiles estimated by this model and those estimated by a prior study’s model, which doesn’t life activity relationship between family members.

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