Research on Optimization Methods for Cost-saving Operation of Self-heating and Heating Systems
With the advancement of the"coal-to-electricity"initiative,electric heating systems have been widely used.However,due to the lack of reasonable operational control methods in the heating systems of some neighborhoods,the issue of high operational costs for electric heating systems has become increasingly prominent.To reduce the operational costs of heating systems,this paper proposes an optimization method for heating system operation cost reduction based on indoor temperature prediction.It treats the hourly output combinations of heat source equipment over a 24-hour period as an operational mode.The GA-BP neural network is used to predict the indoor temperature of each operational mode,thereby determining whether it meets the heating standard.Verification has shown that the maximum error of the GA-BP indoor temperature prediction model established in this paper is 0.7℃,with an average error of 0.07℃,indicating that this model can be used in actual heating systems.Finally,genetic algorithms are used to find the operational mode with the lowest operational cost from among the operational modes that meet the heating standard.Taking a neighborhood in Shanxi Province as an example,the operational costs were calculated according to two different optimization modes and compared with the original operational method.The results showed that,without adjusting the existing indoor temperature,the cost reduction effect was 8.21%.When ensuring that the indoor temperature is not lower than 18℃,a cost reduction of 16.74%can be achieved.
Heating systemRoom temperature predictionHenetic algorithmBP neural networkOptimal energy-saving operation mode
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