A Dynamic and Unique Approach for Energy Consumption Prediction In High Raised Buildings

Abstract

Prediction of power consumption is a crucial chore in vitality conservation. Because of backing vector-basedregression has a good efficiency in coping with non-linear knowledge regression problems, lately it frequently was used to foretell constructing vitality consumption. Primarily according to the historic knowledge its concludedthat the connection between lighting power consumption and its influencing elements is non-linear. The prediction of vitality consumption is a vital activity for power buying and selling companies. The prediction ought to be as correct as doable because the accuracy of the prediction interprets straight into the company’s profit. Electrical hundreds and vitality consumption forecasting are a number of the most vital duties in energy scheme operation and planning. However, in some cases, we have to resolve this drawback within the absence of dependable and enough historic data. To develop a correct prediction mannequin of the lighting power consumption, a support vector regression is used along with a radial basis function. The forecast outcomes point out that the prediction accuracy of support vector regression is larger than neural networks. The prediction model can forecast the constructing hourly power consumption and check the impression of workplace constructing vitality administration plans.