Variable static pressure control method of VAV system based on self-adaptive fuzzy inference

The traditional variable static pressure fuzzy control method of VAV air conditioning system relies on human experience to obtain fuzzy rules, which leads to the problem of incomplete coverage of effective fuzzy rules, resulting in long control time, overshoot and high energy consumption. Proposes a variable static pressure fuzzy control method based on subtractive clustering and self-adaptive neural fuzzy inference system (SC-ANFIS). This method uses the learning ability of subtractive clustering algorithm to cluster the input samples, optimize the input sample data and generate fuzzy rules, and uses the neural fuzzy inference method to train fuzzy rules, to realize VAV variable static pressure fuzzy control. The comparative experiments on a VAV system experimental platform show that compared with the constant static pressure method, this method reduces the power consumption of forced draft fan by 67%. Compared with the empirical variable static pressure fuzzy control method, it features shorter adjustment time, more stable control process and stronger anti-interference, and can reduce the power consumption of forced draft fan by 7%.