Currently, in the building sector there is an increase in energy use due to
the increased demand for indoor thermal comfort. Proper energy planning
based on a real measurement data is a necessity. In this study, we developed
and evaluated hybrid artificial intelligence models for the prediction of
the daily heating energy use. Building energy use is defined by significant
number of influencing factors, while many of them are hard to define and
quantify. For heating energy use modelling, complex relationship between the
input and output variables is not strictly linear nor non-linear. The main
idea of this paper was to divide the heat demand prediction problem into the
linear and the non-linear part (residuals) by using different statistical
methods for the prediction. The expectations were that the joint hybrid
model, could outperform the individual predictors. Multiple Linear
Regression (MLR) was selected for the linear modelling, while the non-linear
part was predicted using Feedforward (FFNN) and Radial Basis (RBFN) neural
network. The hybrid model prediction consisted of the sum of the outputs of
the linear and the non-linear model. The results showed that the hybrid FFNN
model and the hybrid RBFN model achieved better results than each of the
individual FFNN and RBFN neural networks and MLR on the same dataset. It was
shown that this hybrid approach improved the accuracy of artificial
intelligence models.
Hybrid artificial intelligence model for prediction of heating energy use
