Abstract
Introducing integrated, automatic control to buildings operating with the environmental quality management (EQM) system, we found that existing energy models are not suitable for use in integrated control systems as they poorly represent the real time, interacting, and transient effects that occur under field conditions. We needed another high-precision estimator for energy efficiency and indoor environment and to this end we examined artificial neural networks (ANNs). This paper presents a road map for design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. It demonstrates that in creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. It also shows that ANN systems designed in this manner may have a high precision in characterizing the response of the building exposed to the variable outdoor climatic conditions. The absolute value of the relative errors ( M a x A R E ) being less than 1.4% for each stage of the ANN development proves that our objective of monitoring and EQM characterization can be reached.
Highlights
A published review [1] and papers [2,3,4] introduced the concept of environmental quality management (EQM) including a feasibility of application of the selected statistical methods [5] or artificial neural networks [6] to control heating systems
We found that despite the many papers published on artificial neural networks (ANNs), several technical questions related to the resulting precision of the networks, such as effect of initial conditions, weights and bias in the ANN, effect of missing data, shifts in building response related to the outdoor conditions, and outliers caused by misfunction of existing controllers, are not addressed
If the ANN structure, despite changes in the initial weight and bias, maintains consistency in the basic qualifiers such as MARE and R (Pearson’s coefficient), one can assume that the structure is not sensitive to the initial conditions [55]
Summary
A published review [1] and papers [2,3,4] introduced the concept of environmental quality management (EQM) including a feasibility of application of the selected statistical methods [5] or artificial neural networks [6] to control heating systems. This paper is a first attempt to address the full system of monitoring and management of environmental quality in buildings from the control point of view. The problem is two-fold: (1) most of the currently used energy models solve a system of simultaneous heat, air, and water transfer equations that are partial and hysteretic, second-order. One may consider the currently used energy models as less suitable for building an automatic control system
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