Abstract
The paper presents a Machine Learning (ML) approach to household Electrical Energy (EE) consumption prediction. It includes: data preprocessing, feature engineering, learning a classification model, and experimental evaluation on one of the largest datasets for household EE consumption – DataPort dataset. Beside the features extracted on the historical EE consumption, we additionally analyze weather and contextual-calendar related features. We believe that the combination of multiple sources of data (calendar, weather, historical EE consumption) provides more information to the model in order to learn better performing model. The experimental results showed that in all the cases the ML algorithms outperform the baselines, with the best performing the XGBoost - achieved 0.69 RMSE score, 0.41 MAE score and 0.67 R2 score which is significantly better than the best performing baseline model (the value from 24 h ago). Additionally, the results show that the largest errors are made for the weekends, which was expected due to the irregularities in the schedule - trips, vacations, etc.
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Acknowledgment
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. Additionally, we would like to thank the DataPort portal for providing the dataset for research purposes.
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Stankoski, S., Kiprijanovska, I., Ilievski, I., Slobodan, J., Gjoreski, H. (2019). Electrical Energy Consumption Prediction Using Machine Learning. In: Gievska, S., Madjarov, G. (eds) ICT Innovations 2019. Big Data Processing and Mining. ICT Innovations 2019. Communications in Computer and Information Science, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-030-33110-8_7
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