End-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and actions. One of the major barriers to acquiring such heating and cooling information for small- and medium-sized buildings or low-income households is the high cost related to submetering and maintenance. The end-use data, especially for heating and cooling end-uses, of such-sized buildings are a national blind spot. In this study, to alleviate this measurement cost problem, two weather-sensitive energy disaggregation methods were examined: the simplified weather-related energy disaggregation (SED) and change-point regression (CPR) methods. The first is a nonparametric approach based on heuristics, whereas the second is a parametric approach. A comparative analysis (one-way ANOVA, correlation analysis, and individual comparison) was performed to explore the disaggregation results regarding heating and cooling energy perspectives using a measurement dataset (MEA) from eleven office buildings. The ANOVA results revealed that there was no significant difference between the three groups (SED, CPR, and MEA); rather strong correlation was observed (r > 0.95). Furthermore, an analysis of the building-level comparison showed that the more distinct the seasonal usage in the monthly consumption pattern, the lower the estimation error. Thus, the two approaches appropriately estimated the amount of heating and cooling used compared with the measurement dataset and demonstrated the possibility of mutual complements.

In this study, a comparative economic analysis was conducted for typical greenhouses, plant factories with natural and artificial light, and those with only artificial light, based on the insulation, artificial light, and photovoltaic (PV) installation costs. In addition, the results of research on primary energy consumption and greenhouse gas (GHG) emissions from the use of fossil fuels were presented. By comparing the case-wise annual energy consumption, when all energy sources were converted into primary energy consumption based on the applied coefficients for collection, transport, and processing, to unify calculations for different fossil fuel energy sources, the case of the installed PV systems exhibited large reductions, of 424% and 340%, in terms of primary energy consumption and GHG emissions, respectively. Furthermore, electric heating resulted in higher primary energy consumption and GHG emissions than oil. When the economic analysis included the plant factory installation cost used to maintain the temperature required for plant growth in winter, the PV installation exhibited the highest cost; additionally, all plant factories showed an investment payback period of seven to nine years, which is comparable to typical greenhouses. Based on these results, we aim to reduce the use of fossil fuels for sustainable energy by combining architectural technology for improved energy performance in the agricultural environment.

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