Machine Learning Approaches for Smart City Energy Management

Abstract

With the constant increase in population and the growing impact of climate change, energy efficiency on a household and a city-wide level represents a significant key in the process of transformation of smart cities. Recently, machine learning approaches have been proven to be beneficial in addressing several global problems, especially in areas where large amounts of data is available. In this paper, we propose the use of machine learning methods to analyze the energy consumption behavior of households on a daily and seasonal basis, in order to detect the parts of the days and seasons in which they have peak energy consumption. Our machine learning models allow us to segment households according to daily and seasonal behavior into different groups. Both energy suppliers and individual households may benefit from the segmentation carried out in this paper. Energy suppliers can be precisely aware about the expected energy consumption by the different groups of customers, in different parts of the day, by knowing their daily behaviour. They can also precisely target households with energy efficient programs and provide more reliable estimates of energy savings. Individual households can reduce costs by increasing energy consumption during off-peak cheaper tariff periods, and would also potentially be more careful about their behaviour if they knew how efficient their energy consumption is, compared to other households. Therefore, we believe that the analysis in this paper can provide a solid foundation for the construction of an energy efficiency system which is necessary for creating a smart city.