Argiriou Athanassios A, Bellas-Velidis Ioannis, Kummert Michaël, André Philippe
Section of Applied Physics, Department of Physics, University of Patras, GR-256 00 Patras, Greece.
Neural Netw. 2004 Apr;17(3):427-40. doi: 10.1016/j.neunet.2003.07.001.
An artificial neural network (ANN)-based controller for hydronic heating plants of buildings is presented. The controller has forecasting capabilities: it includes a meteorological module, forecasting the ambient temperature and solar irradiance, an indoor temperature predictor module, a supply temperature predictor module and an optimizing module for the water supply temperature. All ANN modules are based on the Feed Forward Back Propagation (FFBP) model. The operation of the controller has been tested experimentally, on a real-scale office building during real operating conditions. The operation results were compared to those of a conventional controller. The performance was also assessed via numerical simulation. The detailed thermal simulation tool for solar systems and buildings TRNSYS was used. Both experimental and numerical results showed that the expected percentage of energy savings with respect to a conventional controller is of about 15% under North European weather conditions.
本文提出了一种基于人工神经网络（ANN）的建筑物水力供热设备控制器。该控制器具有预测能力：它包括一个气象模块，用于预测环境温度和太阳辐照度；一个室内温度预测模块；一个供水温度预测模块；以及一个用于供水温度的优化模块。所有人工神经网络模块均基于前馈反向传播（FFBP）模型。该控制器的运行已在实际运行条件下的一栋实际规模办公楼中进行了实验测试。将运行结果与传统控制器的结果进行了比较。还通过数值模拟评估了性能。使用了用于太阳能系统和建筑物的详细热模拟工具TRNSYS。实验和数值结果均表明，在北欧天气条件下，相对于传统控制器，预期的节能百分比约为15%。
