Load Prediction Based on Multivariate Time Series Forecasting for Energy Consumption and Behavioral Analytics

Abstract

Liable, proficient and ecological cognizant electrical energy consumption activities are becoming a basic need for the consistent smart grid. This paper put forwards the concept of a data mining and intelligent proposed model to scrutinize including predict electrical energy time sequences to discover a number of time-based power consumption patterns. Support Vector Regression (SVR) have been productively employed to resolve non-linear regression and time sequences complications associated with prediction of residential electric energy consumption. Jaya algorithm is used in this paper as the implementation of SVR is greatly reliant on the collection of its constraints. The predicting model is technologically advanced by means of weighted SVR configurations (\(\nu \)-SVR and \(\epsilon \)-SVR). Besides, the Jaya algorithm is deployed to decide the weights resultant to every configuration. An instance of time sequential power consumption information from a residential edifice in Denmark is employed to explicate the execution of the presented configuration. Furthermore, the anticipated model is able to estimate power consumption for half hour and daily time successions data for the similar building. The consequences depict that the proposed model demonstrates developed weight for \(\nu \)-SVR for half hour data. Nevertheless, a sophisticated weight for \(\epsilon \)-SVR is perceived for diurnal data. The Mean Absolute Percentage Error (MAPE) for everyday power expenditure data is 5.521 while for half-hour power utilization is 3.769 correspondingly. Also, a thorough evaluation with different algorithms indicate that the presented configuration produces greater exactness for residential power exhaustion prediction.