Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes

Abstract

Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts.