Anomaly Detection Using Autoencoders for Household Electricity Meters
DOI:
Abstract
Household electricity consumption often exhibits sudden and unexplained spikes that typically go unnoticed until the monthly bill arrives. These anomalies may stem from equipment malfunction, inefficient appliance usage, or irregular electrical patterns that households cannot easily observe. This study proposes an unsupervised anomaly detection framework based on autoencoders to identify abnormal consumption behavior from high resolution household electricity meter data. The model learns normal consumption patterns through reconstruction and flags anomalies using a dynamic threshold derived from reconstruction error distribution. Experimental results demonstrate strong detection capability, particularly for sudden spikes, achieving a precision of 0.92, recall of 0.88, and F1 score of 0.90. The findings highlight the potential of deep learning–based unsupervised methods to support real time, edge deployable solutions for energy efficiency and early fault detection in residential environments.
Downloads
Views : 29
Downloads : 30
References
J. Chew, A. Sharma, D. S. Kumar, W. Zhang, N. Anant, and J. Dong,
“Unveiling the dynamics of residential energy consumption: A quantitative study of demographic and personality influences in Singapore using machine learning approaches,” Sustainability, vol. 16, no. 14, 2024.
C. P. Paneru and A. K. M. Tarigan, “Reviewing the impacts of smart energy applications on energy behaviours in Norwegian households,” Renewable and Sustainable Energy Reviews, vol. 183, 113511, Sep. 2023.
M. M. Naseer, A. I. Hunjra, A. Palma, and T. Bagh, “Sustainable development goals and environmental performance: Exploring the contribution of governance, energy, and growth,” Research in International Business and Finance, vol. 73, no. B, 102646, Jan. 2025.
R. N. Muniz, C. T. da Costa Júnior, W. G. Buratto, A. Nied, and G. V. González,
“The sustainability concept: A review focusing on energy,” Sustainability, vol. 15, no. 19, 2023.
L. N. Tran, Q. Wu, and H. T. Hoang, “The effect of changing heat use patterns on residential energy efficiency in a Japanese smart community,” Scientific Reports, vol. 15, no. 20404, 2025.
D. Novianto, M. D. Koerniawan, M. Munawir, and D. Sekartaji,
“Impact of lifestyle changes on home energy consumption during pandemic COVID-19 in Indonesia,” Sustainable Cities and Society, vol. 83, Aug. 2022.
N. Choudhry, J. Abawajy, S. Huda, and I. Rao, “A comprehensive survey of machine learning methods for surveillance videos anomaly detection,” IEEE Access, vol. 11, pp. 105046–105072, Oct. 2023.
Y. Song, S. Kuang, J. Huang, and D. Zhang, “Unsupervised anomaly detection of industrial building energy consumption,” Energy and Built Environment, in press, Dec. 2024.
C. Yang, H. Li, Y. Ma, Y. Huang, and X. Chu, “Enhanced TSMixer model for the prediction and control of particulate matter,” Sustainability, vol. 17, no. 7, 2025.
K. Hu, C. Shen, T. Wang, et al., “Overview of temporal action detection based on deep learning,” Artificial Intelligence Review, vol. 57, no. 26, 2024.
G. Wang, S. Sun, S. Fan, Y. Liu, S. Cao, and R. Guan, “A spatial–temporal data-driven deep learning framework for enhancing ultra-short-term prediction of distributed photovoltaic power generation,” International Journal of Electrical Power & Energy Systems, vol. 160, 110125, Sep. 2024.
M. Landauer, S. Onder, F. Skopik, and M. Wurzenberger, “Deep learning for anomaly detection in log data: A survey,” Machine Learning with Applications, vol. 12, 100470, Jun. 2023.
H. Park and H. Jang, “Enhancing time series anomaly detection: A knowledge distillation approach with image transformation,” Sensors, vol. 24, no. 24, 2024.
S. Kumari, C. Prabha, A. Karim, M. M. Hassan, and S. Azam, “A comprehensive investigation of anomaly detection methods in deep learning and machine learning: 2019–2023,” IET Information Security, vol. 2024, no. 1, 8821891, Nov. 2024.
M. R. Shadi, H. Mirshekali, and H. R. Shaker, “Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects,” Renewable and Sustainable Energy Reviews, vol. 216, 115668, Jul. 2025.
M. Orabi, K. P. Tran, P. Egger, and S. Thomassey, “Anomaly detection in smart manufacturing: An Adaptive Adversarial Transformer-based model,” Journal of Manufacturing Systems, vol. 77, pp. 591–611, Dec. 2024.
S. Chen and T. Nakachi, “Multi-Channel Gaussian Process Regression for Network Traffic Anomaly Detection via Inter-Channel Correlation,” in Proc. 2025 Int. Tech. Conf. Circuits/Syst., Comput., Commun. (ITC-CSCC), pp. 1–6, 2025.
E. Estrada-Patiño, G. Castilla-Valdez, J. Frausto-Solis, et al., “A Novel Approach for Temperature Forecasting in Climate Change Using Ensemble Decomposition of Time Series,” Int. J. Comput. Intell. Syst., vol. 17, p. 253, 2024.
S. Karamolegkos and D. E. Koulouriotis, “Advancing short-term load forecasting with decomposed Fourier ARIMA: A case study on the Greek energy market,” Energy, vol. 325, p. 135854, Jun. 2025.
S. B. Chudo and G. Terdik, “Modeling and forecasting time-series data with multiple seasonal periods using periodograms,” Econometrics, vol. 13, no. 2, 2025.
C. Prakash, B. Dhyani, A. Chauhan et al., “ARIMA based forecasting of solar and hydro energy consumption with implications for grid stability and renewable policy,” Discover Sustainability, vol. 6, no. 663, 2025.
M. L. Hossain, S. M. N. Shams, and S. M. Ullah, “Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models,” Discover Sustainability, vol. 6, no. 775, 2025.
S.-C. Necula, I. Hauer, D. Fotache, and L. Hurbean, “Advanced hybrid models for air pollution forecasting: Combining SARIMA and BiLSTM architectures,” Electronics, vol. 14, no. 3, 2025.
I. H. Sarker, “Machine learning: Algorithms, real-world applications and research directions,” SN Computer Science, vol. 2, p. 160, 2021.
P. Koukaras and C. Tjortjis, “Data preprocessing and feature engineering for data mining: Techniques, tools, and best practices,” AI, vol. 6, no. 10, 2025.
G. Murariu, L. Dinca, and D. Munteanu, “Trends and applications of principal component analysis in forestry research: A literature and bibliometric review,” Forests, vol. 16, no. 7, Art. no. 1155, Jul. 2025.
M. Taktak and F. Derbel, “Evaluating the impact of frequency decomposition techniques on LSTM-based household energy consumption forecasting,” Energies, vol. 18, no. 10, 2025.
M. A. Yahya, A. R. Moya, and S. Ventura, “Deep learning for multivariate time series anomaly detection: an evaluation of reconstruction-based methods,” Artificial Intelligence Review, vol. 58, 2025.
Y. Ghazi, M. Tabaa, M. Ennaji, and G. Zaz, “An explainable Markov chain–machine learning sequential-aware anomaly detection framework for industrial IoT systems based on OPC UA,” Sensors, vol. 25, no. 19, 2025.
E. Stracqualursi, A. Rosato, G. Di Lorenzo, M. Panella, and R. Araneo, “Systematic review of energy theft practices and autonomous detection through artificial intelligence methods,” Renewable and Sustainable Energy Reviews, vol. 184, 113544, Sep. 2023.
F. Harrou, B. Bouyeddou, A. Dairi, and Y. Sun, “Exploiting autoencoder-based anomaly detection to enhance cybersecurity in power grids,” Future Internet, vol. 16, no. 6, 2024.
H. Ishfaq, S. Kanwal, S. Anwar, M. Abdussalam, and W. Amin, “Enhancing smart grid security and efficiency: AI, energy routing, and T&D innovations (A review),” Energies, vol. 18, no. 17, 2025.
J. Feng, T. Yu, K. Zhang, and L. Cheng, “Integration of multi-agent systems and artificial intelligence in self-healing subway power supply systems: Advancements in fault diagnosis, isolation, and recovery,” Processes, vol. 13, no. 4, 2025.
X. He, T. Du, T. Long, et al., “Signaling cascades in the failing heart and emerging therapeutic strategies,” Signal Transduction and Targeted Therapy, vol. 7, no. 134, 2022.
R. Lin, S. Chen, Z. He, B. Wu, H. Zou, X. Zhao, and Q. Li, “Electricity behavior modeling and anomaly detection services based on a deep variational autoencoder network,” Energies, vol. 17, no. 16, 2024.
R. Liao, M. Manfren, and B. Nastasi, “Off-grid PV systems modelling and optimisation for rural communities—leveraging understandability and interpretability of modelling tools,” Energy, vol. 324, 135948, Jun. 2025.
N. H. A. Mutalib, A. Q. M. Sabri, A. W. A. Wahab, et al., “Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review,” Artificial Intelligence Review, vol. 57, p. 297, 2024.
R. J. L. Taloma, F. Cuomo, D. Comminiello, et al., “Machine learning for smart water distribution systems: exploring applications, challenges and future perspectives,” Artificial Intelligence Review, vol. 58, p. 120, 2025.
G. Chen, S. Lu, S. Zhou, Z. Tian, M. K. Kim, J. Liu, and X. Liu, “A systematic review of building energy consumption prediction: From perspectives of load classification, data-driven frameworks, and future directions,” Applied Sciences, vol. 15, no. 6, 2025.
K. O. Ukoba, K. O. Olatunji, E. Adeoye, T.-C. Jen, and D. M. Madyira, “Optimizing renewable energy systems through artificial intelligence: Review and future prospects,” Energy & Environment, vol. 35, no. 7, May 2024.
