A neural network for monitoring and characterization of buildings

This paper continues work from part 1 where a high precision estimator for energy efficiency and indoor environment based on artificial neural networks (ANN) was examined. Part 1 demonstrated that creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. Now, we extend our requirements for the model to be rapid and precise. At the end of this work we obtain a road map for the design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. This paper also shows that ANN system designed may have a high precision in characterizing the response of the building exposed to variable outdoor climatic conditions. The absolute value of the relative errors, MaxAR, is less than 2%. It proves that monitoring and ANN-based characterization approach can be used for different buildings, including those with the best environmental performance.