Energy conservation in buildings through efficient A/C control using neural networks

Abstract

General regression neural networks (GRNNs) were used to optimize air conditioning setback scheduling in public buildings. To save energy, the temperature inside these buildings is allowed to rise after business hours by setting back the thermostat. The objective is to predict the time of the end of thermostat setback (EoS) such that the design temperature inside the building is restored in time for the start of business hours. State-of-the-art building simulation software, ESP-r, was used to generate a database that covered the past 5 years. The software was used to calculate EoS for two office buildings using the climate records in Kuwait. The EoS data for 1995 and 1996 were used for training and testing the neural networks (NNs). The robustness of the trained NN was tested by applying them to a “production” data set (1997–1999) which the networks have never “seen” before. A parametric study showed that the optimum GRNN design is one that uses a genetic adaptive algorithm, a so-called City Block distance metric, and a linear scaling function for the input data. External hourly temperature readings were used as network inputs, and the thermostat end of setback (EoS) is the output. The NN predictions were improved by developing a neural control-scheme. This scheme is based on using the temperature readings as they become available. Six NNs were designed and trained for this purpose. The performance of the NN analysis was evaluated using a statistical indicator (the coefficient of multiple determination), and by examination of the error patterns. The results show that the neural control-scheme is a powerful instrument for optimizing air conditioning setback scheduling based on external temperature records.