Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks

Two NN based energy-consumption models were developed to estimate the space and domestic hot-water heating energy consumptions in the Canadian residential sector and the NN methodology used in developing the models, the accuracy of the predictions, and some sample results are presented.

Abstract

Two methods have been used to model residential end-use energy consumption at the national or regional level: the engineering method and the conditional demand-analysis method. It was recently shown that the neural network (NN) method is capable of accurately modeling the behaviours of the appliances, lighting, and space-cooling energy consumption in the residential sector. As a continuation of the work on the use of the NN method for modeling residential end-use energy-consumption, two NN based energy-consumption models were developed to estimate the space and domestic hot-water heating energy consumptions in the Canadian residential sector. This paper presents the NN methodology used in developing the models, the accuracy of the predictions, and some sample results.