Understanding the energy consumption patterns of buildings and investing efforts toward energy load reduction is important for optimizing resources and conserving energy in buildings. In this research, we proposed a forecasting method for the electricity load of university buildings using a hybrid model comprising a clustering technique and the autoregressive integrated moving average (ARIMA) model. The novel approach includes clustering data of an entire year, including the forecasting day using K‐means clustering, and using the result to forecast the electricity peak load of university buildings. The combination of clustering and the ARIMA model has proved to increase the performance of forecasting rather than that using the ARIMA model alone. Forecasting electricity peak load with appreciable accuracy several hours before peak hours can provide the management authorities with sufficient time to design strategies for peak load reduction. This method can also be implemented in the demand response for reducing electricity bills by avoiding electricity usage during the high electricity rate hours.

Energy demands in the building sector account for more than 30% of the total energy use and more than 55% of the global electricity demand. Efforts to develop sustainable buildings are progressing but are still not keeping pace with the growing building sector and the rising demand for energy. Analyzing the energy use pattern of buildings and planning for energy conservation in existing buildings are essential. In this research, we propose a method to analyze the energy use pattern in a building using the K-means clustering method. Initial centroids in K-means clustering are chosen randomly so that the clustering result changes every time. This instability is removed in the proposed method by the selection of initial centroids using a percentile method based on empirical cumulative distribution. The results from the proposed method have better accuracy, and the internal cohesion and separation between clusters are better than the random initialization method. Analyzing yearly electricity use using the proposed clustering method, the daily pattern of electricity use can be categorized according to the operation of buildings. For this purpose, in this research, electricity use pattern was analyzed for three to six clusters. In comparison with the university schedule, six clusters were found to be appropriate and the accuracy was 89.3%. Once daily electricity use are categorized, base electricity consumption, electricity consumption by human activities, and electricity consumption by air-conditioning can be determined. As energy consumption by usage is clarified, measures for energy consumption in university buildings can be proposed.

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The energy demand of the building sector is increasing rapidly, driven by the improved access to energy in developing countries, greater ownership and use of energy-consuming devices, and rapid growth in building floor area. Energy demands in the building sector account for more than 30% of the total energy consumption and more than 55% of the global electricity demand. Efforts to develop sustainable buildings are progressing but are still not keeping up with the growing building sector and the rising demand for energy. Analyzing the energy consumption pattern of the buildings and planning for energy conservation in existing buildings are essential. In this research we proposed a method to analzse the energy pattern of university buildings using K-means clustering method. Energy consumption in Science, non-science and office buildings of university is analyzed and their respective base energy, energy consumption due to human activities and air-conditioning energy consumption is calculated. The proposed method is successful in classifying the energy consumption and will prove to be helpful in the planning of energy conservation in buildings.

Results of summer survey on two facilities in Kasugai city 1 2 3 4 Nepal Bishnu *5Aya YOKOE, Megumi MITSUDA, Toshimi TANAMURA, Motoi YAMAHA and Bishnu NEPALWe investigated the thermal-odor environment of elderly facility. The amount of activity differs between elderly and caregivers, it is not necessarily comfortable for caregivers even if they are comfortable for the elderly. Further, for caregivers, there is a possibility of feeling uncomfortable if the room temperature is high even under the same PMV. A caregiver with a high activity level may deteriorate the odor environment by sweating. Odor components by humans may be removed managing temperature or increasing the amount of ventilation. We concluded that a ventilation frequency of 2 / h is appropriate under a comfortable thermal environment.