A Dynamic and Unique Approach for Energy Consumption Prediction

Abstract

Prediction of electricity utilization is a significant errand in energy preservation. Becausesupport vector regression has great implementation in managing non-linear data regression issue, as of late it regularly was utilized to foresee building energy utilization. In view of the chronicled information we close the connection between lighting energy utilization and its impacting factors is non-linear. The forecast of energy utilization is a significant undertaking for energy exchanging organizations. The expectation ought to be as exact as conceivable since the precision of the forecast makes an interpretation of straightforwardly into organization's benefit.

Electrical loads and energy utilization forecasting is one of the most significant task in power system operation and planning. In any case, at times we have to take care of this issue without reliable and verifiable data.

To make precise forecast model of lighting energy use, the support vector regression with spiral reason work was applied. The gauge results show that the prediction precision of support vector regression is higher than neural networks. The prediction model can conjecture any structure's hourly energy use and study the effect of any building or structure's energy the executives’ plans.