مدلسازی نصب حرارت مرکزی در مقیاس کوچک با استفاده از شبکه های عصبی مصنوعی با هدف مصرف برق پایین

Artificial neural networks (ANNs) used to control the operation of energy systems is an important field of research. This paper deals with the use of ANNs as a technique of modelling real non-linear energy systems such as the flow and pressure processes related to pump and valve input voltages of a small scale central heating system aiming at low electric energy consumption. The system is located in the Energy Economics Laboratory of Democritus University of Thrace in Greece and its operational parameters were accurately captured using a backpropagation neural network. The approach described in this paper has the advantages of computational speed, low cost for feasibility and ease of design by operators.

In 1995, the Greek Ministry of Environment, Urban Planning and Public Works prepared an Action Plan, entitled “Energy 2001”, aiming at promoting the application of energy-efficiency technologies, in the building sector. The Action Plan was prepared in order to define specific measures for the reduction of greenhouse gas emissions in buildings, in accordance with the “National Action Plan for the Abatement of CO2 and Other Greenhouse Gases”. Following official adoption of the Action Plan by the Greek Government, “Energy 2001” was further reinforced by the enactment of Ministerial Decree (MD) 21475/98, which incorporated the provisions of Council Directive 93/76/EC (SAVE Directive) for the stabilisation of CO2 emissions and the efficient use of energy in buildings [1]. Space air-conditioning dominates the energy consumption in residential and public building sector [2], [3] and [4]. In Greece, the largest percentage of buildings (old and new) is using the classic oil-based central heating installation with water as heat transfer agent. The effective operation control of these central heating installations, based on the monitoring of different operational and performance parameters, leads to substantial energy saving reducing simultaneously the environmental pollution and the need for further capital investment in power plants construction. The use of ANNs in energy systems can be viewed as a natural step in the evolution of control methodology. This is mainly due to the fact that ANNs have good approximation capabilities and offer additional advantages such as short development and fast processing times [5]. A detailed description of various applications of ANNs in energy systems is provided by Kalogirou [6]. In particular, ANN applications to building sector have attracted considerable attention from the scientific community [7], [8] and [9].

The purpose of this project was to investigate the use of neural networks as a technique of modelling non-linear energy systems and to apply this technique in a real laboratory system (small scale central heating system). The main objective was to develop a neural network and train it in order to approximate the flow and pressure processes related to the pump and valve input voltages. Critical issues were: (a) the selection of the architecture of the neural network, (b) the implementation of the Levenberg–Marquardt backpropagation in MATLAB, (c) the development of virtual instruments in LABVIEW for data collection and monitoring of the heating system and (d) the choice of the excitation method for the generation of maximally informative data for the network. Analysis of the dynamic response was performed and the step response characteristics of flow and pressure of the heating system were obtained in order to understand its operation and to obtain the genetic dynamic characteristics and the type of non-linearity of the processes involved. By applying a set of different steps into the system, the values of the time constant and the dead-time were found to vary because of the non-linearities present in the system which were mainly due to the flow of water trough the pipes and the differential pressures this was causing. Calibration tests were also performed for the pressure and flow meter circuits. In the development of the neural model, there were a number of issues that needed consideration such as the network topology, training strategy and validation. It was found that a two-layer back-propagation network with 4 tansigmoid neurons in the hidden layer and a linear output layer was a successful network design. The network was trained using the Levenberg–Marquardt back-propagation algorithm.