Very Short-Term Electrical Energy Consumption Forecasting of a Household for the Integration of Smart Grids


The recent integration of smart grid systems to present electric power systems and the increasing penetration of renewable energy sources make electrical energy consumption forecasting not only a prominent subject but also an arduous challenge due to nonlinear and nonstationary characteristics of electric loads which can be affected by seasonal effects, weather conditions, socioeconomic dynamics, and random effects. Very short-term electrical energy consumption forecasting (VSTCF), which includes few minutes to an hour ahead forecasting of electrical energy consumption, provides monitoring energy consumption, finding base and peak loads, making viable decisions for renewable energy investments such as photovoltaic (PV) systems, and enhancing energy management quality of a household for smart grid integration. In this paper, it is the first time in Turkey, average of 5-minute electrical energy consumption data of a household will be obtained by an energy logger during 1 month period in order to perform VSTCF by using several artificial intelligence (AI) topologies in the literature. After data pre-processing, different AI techniques will be applied to real-time data obtained from a household in the Mediterranean Region of Turkey for the calculation of performance metrics such as mean absolute percentage error (MAPE) and root mean square error (RMSE).

Author Information
Kasım Zor, Adana Science and Technology University, Turkey
Oğuzhan Timur, Çukurova University, Turkey
Özgür Çelik, Adana Science and Technology University, Turkey
Hatice Başak Yıldırım, Adana Science and Technology University, Turkey
Ahmet Teke, Çukurova University, Turkey

Paper Information
Conference: ECSEE2018
Stream: Energy: Energy Economics and Ecological Economics

This paper is part of the ECSEE2018 Conference Proceedings (View)
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