Date of Publication :22nd February 2018

Abstract: The present era is the digital era and digital technologies are many impacts on human life as well as societies. Starting from Smart city design to social networking peoples are now using digital technologies. As the use of technologies grows the demand for its source that is electricity grows. Predication of electricity demand is a crucial problem and providing a solution through statistical rules is a big challenge. Present day Electricity demand pattern is considered to play a crucial role in the modernization of community. So, forecasting of household energy consumption precisely is essential due to the fluctuating demand –response of energy. It is also considered very significant for energy planning, development mechanism and economic success. In this paper, Statistical technique model is used to predict future consumption demand of electrical consumption rate. The prediction with output likely sequence works significantly by the implementation of Hidden Markov Model (HMM) using the Viterbi Algorithm with emphasis is given to forecasting future energy consumption demand where data pattern changes monthly and shows non-linear trends. This paper uses an extensive data sample of author’s working university database. The final forecasted outcome is tested and compared with actual data. Experimental results show an aggregate of 0.0366 error rate showing non–linear trends of household electricity consumption with respect to factors concerning such as population, climatic conditions and financial strength. The prediction model based on the small-scale fraction of households summarizing for most likely aggregate consumption response. The proposed model helps in recognizing future electricity consumption pattern which will be used in smart city design and provide a solution for electrification of urban and rural as well as any planning in government and non-government organizations for electricity consumption.

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