# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 4 Conclusions  
The research conducted in this paper considers addressing the modern home appliance market's response to the diversification of consumer demand. Focusing on the demand forecasting of air conditioning products, the authors of this paper proposed a combined forecasting model, LSTM-RF-XGBoost, which was then compared to traditional machine learning models and classical deep learning single forecasting models. The influences and performances of different models for the demand forecasting of air conditioning products were discussed. The data showed that the MAE, RMSE, and R\({}^{2}\) values of the combined LSTM-RF-XGBoost model were 0.0658, 0.1407, and 0.8682, respectively, lower than the single model. In the future, it is still necessary to further verify the adaptability and stability of the model on different home appliance datasets and datasets of different sizes. Other datasets may have different feature distributions and data structures compared to the appliance product dataset. For example, if the new dataset involves a different type of product or service, its features and attributes may differ from the air conditioning product dataset, and the model needs to be adjusted to reflect these new data features. For large datasets, strategies like distributed computing and parallel processing are considered to improve the efficiency of model training and prediction, thereby enhancing their scalability. For limited datasets, data enhancement techniques are employed to improve the generalization ability and stability of models, as well as to address the model overfitting problem. In addition, techniques such as migration learning can be considered to transfer the model parameters trained on large datasets to small datasets, accelerating model training and improving prediction. Throughout the migration process, models must consider data quality and labeling accuracy. If the new dataset is of poor quality or the labels are incorrect, it may affect the performance and generalization ability of a model. Therefore, adequate data preprocessing and labeling adjustments are required when migrating models to ensure that they can accurately capture the features and patterns of the new dataset. Future research will involve the further collection and collation of datasets of different types of household appliances, such as washing machines, refrigerators, televisions, etc., in order to evaluate the applicability of our model on data from different home appliances. To change the model parameters and optimize the model structure, it is required to consider the feature differences and data distribution among household appliances. It is essential to conduct comprehensive model evaluation and validation procedures to ascertain the model's adaptability and transferability across diverse home appliance datasets. Additionally, the model's inadequacies are apparent in its failure to adequately take external environmental aspects into account. Economic conditions, regulatory changes, societal trends, geographical disparities, and emergencies, for example, can all have a substantial impact on the demand for household appliances. Specifically, macroeconomic indicators, such as the GDP growth rate and unemployment rate, affect consumers' purchasing power, policy changes such as energy-saving subsidies and environmental regulations change market demand, social trends and consumer preferences influence the popularity of home appliances, and demand for air conditioners in the cold north may be lower than in the hot south. Furthermore, unexpected events such as natural disasters and epidemics might have an unanticipated effect on demand. As a result, the model should incorporate economic indicators, policy information, social trend data, and geographical data into the forecasting process using feature engineering and model adjustment.