空调控制参数用户设定偏好的预测方法
摘要：空调器智能化提升的方向，是使平时需要用户设定的控制参数值自动地调整为用户的偏好值。这就需要空调器能够准确预测出用户偏好的设定参数，在实现上则有赖于对用户历史操作数据的学习，但这又超出了空调器单片机的计算能力。本文提出云端学习及本地计算相结合的空调用户偏好的预测方法，将学习用户历史操作数据等复杂的计算任务放到云端服务器，使得对于控制参数的预测不会超过空调器单片机的计算能力。该方法中，云端服务器完成对用户历史数据的预处理和递归式特征消除后，使用梯度提升框架训练数据并得到学习模型；空调器单片机下载由云端生成的多维矩阵，建立对预测值的插值查询规则后即得到本地预测方法。本文在对上述建立的学习算法和预测方法进行验证时，将上海、重庆和广州3个不同地域的城市的用户夏季使用数据(不含8月)作为训练集，8月的数据作为测试集。验证结果表明：用户实际设定温度与预测值的误差在±0.5℃内的占比平均为84%,最高为88%;用户实际设定风速与预测值的误差在±10%内的占比平均为92%,最高为94%。验证结果表明本文提出的云端学习及本地计算相结合的用户偏好的预测方法能够准确地预测用户偏好。
关键词：房间空调器控制参数用户偏好数据挖掘机器学习偏好预测
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[2] ZHOU H,QIAO L F,JIANG Y,et al.Recognition of air-conditioner operation from indoor air temperature and relative humidity by a data mining approach[J].Energy and buildings,2016,111:233- 241.
[3] 张静，李念平，周淋萱，等.办公室热舒适与空调运行特点研究[J].暖通空调，2020,50(5):14- 18.
[4] ZHAO D Y,ZHONG M,ZHANG X.Energy consumption predicting model of VRV (variable refrigerant volume) system in office buildings based on data mining[J].Energy,2016,102:660- 668.
[5] 王路瑶，吴斌，杜志敏，等.基于长短期记忆神经网络的数据中心空调系统传感器故障诊断[J].化工学报，2018,69(增刊2):252- 259.
[6] 曾宇柯，陈焕新，黄荣庚.基于局部异常因子结合神经网络的制冷剂充注量故障诊断[J].制冷技术，2019,39(1):6- 10,15.
[7] 李鑫悦，陈淑琴，李鸿亮，等.应用数据挖掘的高校教学建筑空调使用及其能耗分析[J].浙江大学学报(工学版),2020,54(9):1677- 1689.
[8] 陈焕新，孙劭波，刘江岩，等.数据挖掘技术在制冷空调行业的应用[J].暖通空调，2016,46(3):20- 26.
[9] REN X,YAN D,WANG C.Air-conditioning usage conditional probability model for residential buildings[J].Building andenvironment,2014,81:172- 182.
[10] 谢宜鑫.基于机器学习的建筑空调能耗数据挖掘和模式识别[D].北京：北京交通大学，2019:54- 56.
[11] 张弘韬，陈焕新，李冠男，等.主元分析用于多联式空调系统传感器故障检测和诊断[J].制冷学报，2017,38(3):76- 81.
[12] 陈焕新，刘江岩，胡云鹏，等.大数据在空调领域的应用[J].制冷学报，2015,36(4):16- 22.
[13] MAVROMATIDIS G,ACHA S,SHAH N.Diagnostic tools of energy performance for supermarkets using artificial neural network algorithms[J].Energy and buildings,2013,62:304- 314.
[14] KE G L,QI M,FINLEY T,et al.LightGBM:a highly efficient gradient boosting decision tree[C]//31st Conference of Neural Information Processing Systems (NIPS 2017),2017:1- 9.
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Prediction method of air conditioner control parameters by user's preferences
Abstract: The direction of intelligent optimization of the air conditioner is to automatically adjust the control parameter that usually needs to be set by the users to their preferred value. This requires the air conditioner to be able to accurately predict the setting parameters based on users' preferences. This implementation depends on the learning of the users' historical operation data, which is beyond the computing power of the microcomputer in the air conditioner. This paper proposes a prediction method for users' preferences by combining cloud learning and local computing. This method puts the complex computing tasks such as learning users' historical data on the cloud server, so that the prediction of control parameters will not exceed the computing power of the microcomputer in the air conditioner. In this method, after the cloud server completes the preprocessing of historical data and the recursive feature elimination, the gradient boosting framework will be used to train the data and obtain the learning model. The microcomputer downloads the multi-dimensional matrix generated by the cloud, establishes the interpolation query rule, and obtains the local prediction method. When validating the learning algorithm and prediction method established above, summer data(excluding August) in three different regions including Shanghai, Chongqing, and Guangzhou is chosen as the training set, and August data is chosen as the test set. The validation result shows that the error between the actual set temperature and the predicted value within ±0.5 ℃ accounts for an average of 84% and a maximum of 88%. The error between the user's actual set wind speed and the predicted value within ±10% accounts for an average of 92% and a maximum of 94%. Therefore, the prediction method combined with cloud learning and local computing proposed in this paper can accurately predict users' preferences.
Keywords: room air conditioner; control parameter; user preference; data mining; machine learning; preference prediction;
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