Prediction method of air conditioner control parameters by user’s preferences

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.