Engineering, 2013, 5, 108-114 doi:10.4236/eng.2013.51b020 Published Online January 2013 (http://www.SciRP.org/journal/eng) Copyright © 2013 SciRes. ENG Seasonal Regression Models for Electricity Consumption Characteristics Analysis Yusri Syam Akil 1,2 , Hajime Miyauchi 1 1 Department of Frontier Technology for Energy and Devices, Kumamoto University, Kumamoto, Japan 2 Department of Electrical Engineering, Hasanuddin University, Makassar, Indonesia Email: yusuri@st.cs.kumamoto-u.ac.jp, miyauchi@cs.kumamoto-u.ac.jp Received 2013 Abstract This paper presents seasonal regression models of demand to investigate electricity consumption characteristics. Elec- tricity consumption in commercial areas in Japan is analyzed by using meteorological variables, namely temperature and relative humidity. A dummy variable for holidays is also considered. We have developed models for two levels of period to analyze demand characteristics, that is, half year models and seasonal models. Some options for each model are calculated and validated by statistical tests to obtain better models. As results, half year and seasonal models present explicit information about how the variables affect the demand differently for each period. These specific information help in analyzing characteristics of studied commercial demand. Keywords: Commercial Area; Demand Characteristics; Regression Model; Seasons; Relative Humidity; Temperature 1. Introduction In general, electricity consumption analyses such as cha- racteristics investigation and forecasting can provide much information related to the time variance of demand. The result of demand analysis is useful for electric utili- ties in many aspects, for instance, to manage and control their systems more effective. Therefore, it is valuable to analyze an electricity demand in detail through develop- ment of demand models. A number of methods can be used for a demand analysis, and one of them is regres- sion analysis. As a tool, a regression model needs a number of data (dependent and explanation variables). The implementation of proper explanation variables is required to get a good model. As an explanation variable, meteorological parameters such as temperature, humidity, wind speed, and so on, are commonly used and con- firmed electricity demand effectively. Prior studies which employed meteorological parameters and regres- sion models for electricity demand can be found in ref- erences such as [1-4]. Reference [1] develops a demand model using a stepwise procedure to forecast Spanish daily electricity demand. Reference [2] develops regres- sion equations to analyze electricity consumption for residential area in Hong Kong by using climatic and economic variables. Reference [3] develops the model of electricity consumption for residential area in Bangkok Metropolis and analyzes effect of climatic and economic factors for demand. Meanwhile, in [4], authors have de- veloped two statistical models for demand in Greece, namely daily and monthly models to forecast demand up to 12 months ahead (mid-term demand). As electricity demand may differ to time [2,3] and place generally, we have an interest to analyze demand characteristics for commercial area in a typical city in Japan by developing demand models. This study also aims to find the electricity consumption characteristics in Japan. We analyze demand characteristics based on seasonal periods for commercial area in Japan. To achieve the aim, two demand model of two period levels based on different time length, namely half year models (CTEChy1, and CTEChy2) and seasonal models (CTECSM, CTECA, CTECW, and CTECS) are proposed to reveal further demand characteristics by regression analysis. They are developed from an initial model (CTEC) that is derived from all data (whole period) with the same explanation variables and statistical validation processes. In the context of this study, the application of regression approach is effective enough. Beside simple in composing the models, the obtained regression coefficients and statistics contain specific information about the direct relationship between variables and demand. It may be useful to draw a seasonal strategy and to meet demands in maintaining power system performance.