This paper presents results from the second phase (45 days, from mid-January to February, 2014) of a trial to evaluate the impact of peak saving interventions: (1) a tiered rate with increasing-tier prices that apply to usage during each 30-minute period, (2) real-time feedback on electricity usage via an in-home display, (3) weekly reports that provide neighbor comparison and peak saving information, and (4) an email alert to reduce peak usage. Through a randomized experiment, we found that the total average treatment effect of the four measures was around 10% on the condominium’s peak time (7-10pm), which was statistically significant. The saving effects have been eventually increasing through the trial, implying the influences of the awareness change on the interventions over time, or the seasonal difference of energy efficiency behaviors. In addition, the results from analyzing air conditioners’ usage data suggested that roughly half of peak saving is likely to be from saving on air conditioner usage, for example by decreasing temperature setting. Furthermore, the interventions had very small impacts on the households’ gas usage at most, implying that gas usage were not stimulated by our intervention for saving electricity usage.

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Regional policies toward “smart communities” or “compact cities” are garnering attention as the measures for reducing carbon emission. In this paper, we address the effects of treating a district as a unit of an energy demand-and-supply system, focusing the energy supply side. We target urban districts of several square kilometers, having populations of several tens of thousands, which are typical of densely-inhabited districts in Japan.
The methodology for evaluating energy system has two parts: an areal energy system model and a building layout generation model. The areal energy system model is improved from the traditional optimization-type energy models to enable the evaluation of a district’s building layout with consideration to its energy service demand distribution. The building layout generation model generates various imaginary building layouts within districts that have the same population size and climate conditions. The model result shows that compact building layout helps to reduce energy network infrastructure costs while it has little additional positive effect in reducing energy consumption and carbon emissions in terms of the energy supply system.

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This paper describes the performance of a domestic hot water (DHW) supply and space heating system using CO₂ heat pump (HP). Experiments were carried out under the same hot water demand by using the M1 standard mode. As a result of experiments, each coefficient of performance of the HP (HPCOP) was 4.91 in summer, 4.34 in the middle season and 2.75/2.82 in winter (high/low space heating load), respectively. Each system coefficient of performance (SCOP) was 3.59, 3.35 and 2.06/1.94. As a result of the comparison with the performance of HP for DHW only, it was shown that a space heating operation influenced a drop of SCOP. A rise of HP inlet temperature and increase of the heat loss from a hot water storage tank became the factor. As a result of having carried out an approximate evaluation, a value of the degradation ratio of SCOP compared with HP for DHW only was 20% in Tokyo.

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A numerical weather prediction model (NWP) has become a useful tool for a forecast of photovoltaic power generation in an efficient operation of an energy management system (EMS). However, forecast products obtained by the NWP have certainly forecast errors. Before using forecast datasets of solar irradiance (or global horizontal irradiance, GHI), we have to understand error characteristic of GHI forecasts from the NWP.
In this study, we validated GHI forecasts obtained from a local forecast model (LFM) of the Japan Meteorological Agency (JMA) using the surface-observed GHI data at the JMA stations. The LFM is a high-resolution NWP with horizontal grid spacing of 2km. First of all, case studies of GHI forecasts were conducted for different weather conditions. In cases of clear sky conditions and/or spatially large clouds, hourly-forecasted GHI values were significantly close to hourly observed-GHI values. However, significant forecast errors tended to be large in cases of high-level clouds (cirrus) and/or cumulus clouds. Seasonal and regional variations of GHI forecast errors were found from evaluations of forecast errors. Compared to a mesoscale model (MSM, with horizontal grid spacing of 5km) GHI forecasts, regional characteristics of LFM forecast errors were different from MSM forecast results.

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