Identifying the Dominant Features in Indonesia Smart Home Dataset by Interpreting Electrical Energy Consumption Prediction Results
Keywords:
Dominant Feature, K-Nearest Neighbors (KNN), Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Smart HomeAbstract
Smart Home needs convergence between Machine Learning (ML) and IoT to make predictions, which means ML becomes the optimal prediction model for prediction and Interpretation. Electrical Energy Consumption is a critical problem that needs to be predicted and interpreted. The proposed study aims to find the dominant feature for the Indonesia Smart Home Dataset and prediction using K-Nearest Neighbors (KNN) with Hyperparameters (k and Distance Algorithm). The dominant feature is interpreted using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The experiment’s optimal prediction model is validated using error evaluation parameters such as RMSE, MSE, and MAE. The model results (k = 2 and Manhattan Distance) were obtained with RMSE = 0.158, MAE = 0.115, MSE = 0.025, and Manhattan Distance. Although LIME cannot interpret the feature as global, the dominant feature can be displayed globally using SHAP. The global interpretation SHAP result is that the "AC", "Washing Machine", "Lamp", "Water Pump", and “RiceCooker” must be reduced to reduce energy consumption. The KNN learning algorithm can build the model with (k=2 and Manhattan Distance) and SHAP model interpretation. Further research is needed to search for other hyperparameters based on search algorithms to maximize KNN performance.
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