基于关联规则分析的用能系统控制优化研究

Control optimization of energy-using system based on association rule analysis

负荷侧暖通能耗占建筑总用能比值较大,采用合适的调控方案可有效减少用能系统能耗。目前负荷侧暖通节能调控的方法多集中于负荷预测控制,需结合多个模型预测结果且对其精度要求较高,而关联规则分析方法可直接学习历史运行数据的规律,更易得到更节能的调控策略。以武汉某商场用能系统为研究对象,采集系统负荷侧六月份运行数据进行研究。对数据进行清洗和离散化,利用Apriori算法计算功率与设备启停及频率间的关联规则,并依据此给出节能调控方案。研究结果表明,Apriori算法能够很好地学习用能系统历史运行数据的规律,分析系统功率与设备频率、启停等可调参数的关系,能够在实际使用时,更快速、便捷、直接地得到可供参考的系统节能调控策略。

Heating,ventilating and air condition(HVAC)energy consumption on load side accounts for a large proportion of total energy consumption of the building.Adopting appropriate control scheme can effectively reduce energy consumption of energy consumption sys⁃tem.At present,the methods of energy saving regulation and control of HVAC on load side mostly focus on load forecasting control,which requires the combination of multiple model prediction results and high accuracy requirements.However,association rule analysis method can directly learn the law of historical operation data,and can more easily obtain more energy-saving regulation strategies.Energy con⁃sumption system of a shopping mall in Wuhan is taken as the research object,and operating data of the system load side in June are collect⁃ed for research.After the data is cleaned and discretized,Apriori algorithm is used to calculate the association rules between power and de⁃vice start-stop and frequency,and the energy-saving control scheme is presented based on this.Research results show that Apriori algo⁃rithm can learn the law of historical operation data of energy consumption system well.By analyzing the relationship between system power and adjustable parameters such as frequency,start and stop,the energy-saving control strategy can be obtained more quickly,conveniently and directly in actual use.