IntelligEnSia based electricity consumption prediction analytics using regression method

Energy sustainability is one of the world focuses today. We have built our solution which is called IntelligEnSia (Intelligent Home for Energy Sustainability) that is focused on the prediction analytic using Web and Android technology platforms. In this case, to predict the energy consumption we applied three regression models: simple linear regression, KLM a and KLM b. All models can be applied to predict the next period of energy consumption based on the independent variable of X = day and dependent variables of Y = current, voltage, and power. It can be concluded that KLM a, has the smallest error accuracy among the proposed models. It means that, processing the data of similar period and category in a history, has bigger influence to the prediction value. Based on the testing, it is find out that the biggest error percentage among the models is relied on power, while the smallest is relied on current. These three models are valuable to help the decision maker in creating the better energy management in the city regarding the supply and availability.