Design of energy saving control method based on adaptive reinforcement learning for HVAC temperature regulation
Current rule-based HVAC energy-saving control methods are often static,and the formulation of control rules depends on the experience of engineers or equipment administrators,while reinforcement learning energy-saving control methods do not rely on the experience of engineers and can be adaptive learning.Therefore,for the old buildings lacking historical data,the control method based on reinforcement learning has more research value.Therefore,an energy saving control method based on adaptive reinforcement learning for HVAC temperature regulation is proposed.By introducing equivalent thermal parameter modeling method,the thermody-namic model of heat pump temperature regulation process is established,so as to analyze the problem of HVAC energy consumption control.Based on the analysis of the supply link in the power consumption process,the ideal HVAC temperature change calculation under the condition of user demand response is realized by combining the three factors of peak-valley electricity price,incentive sub-sidy and user's appropriate temperature.Based on the fairness of load response,combined with the calculation method of ideal temper-ature variation and relative temperature distance,the reinforcement learning algorithm is designed to plan the temperature adjustment sequence of HVAC host,and realize the control of HVAC heat pump,so as to realize the energy-saving control under adaptive learn-ing.The experimental results show that the proposed method can achieve accurate control of indoor temperature under the premise of low energy consumption,which is of great significance for the development of HVAC technology.