A Systematic Approach for Exploration, Behavior Analysis, and Visualization of Redundant Data Anomalies in Smart Home Energy Consumption Dataset

K. Purna Prakash, Y. V. Pavan Kumar

Abstract


The increase of smart home culture for improved efficiency and comfort in the present energy sector requires paying much attention to big data analytics. Here, the data refers to the energy consumption readings that are continuously captured through smart meters and transmitted to the central computing centres. The entire analysis and decision making in such cases depend on the availability of quality data. However, this data often contains anomalies such as redundancy (duplicated data), which affects their quality. Thus, a systematic approach with three steps (exploration, behavior analysis, and visualization) is proposed in this paper to precisely analyze the redundant data anomalies and their behavior. In exploration, the identification and quantification of redundant data anomalies will be done for all appliances for all available days. This provides the information of the highest and lowest counts of redundancies for all appliances. In behavior analysis, the behavior of redundant data anomalies during various parts of a day will be analysed. The visualization finds the occurrence of redundant data anomalies at the day/hour/minute level. Altogether, these three steps provide a comprehensive analysis of redundant data anomaly behavior that is present in the smart home energy consumption dataset. For the analysis, this paper considers a real-time smart home dataset ‘Tracebase’. From this dataset, the appliance ‘WaterKettle’ is used as an example for the proposed analysis as it exhibits the highest redundancy count when compared to all other appliances. Form the implementation of the proposed approach, it is revealed that there is a high occurrence of redundancy during Daylight hours and is visualized.

Keywords


Behavior analysis; Data analysis; Energy consumption data; Redundant data anomaly; Smart home; Visualization

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References


A. Zielonka, M. Wo?niak, S. Garg, G. Kaddoum, M. J. Piran and G. Muhammad, “Smart homes: how much will they support us? a research on recent trends and advances,” IEEE Access, vol. 9, pp. 26388-26419, 2021.

Y. Himeur, G. Khalida, A. Alsalemi, B. Faycal and A. Amira, “Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives,” Applied Energy, vol. 287, pp. 116601, 2021.

O. Simona-Vasilica, B. Adela, T. B. George, C. I. Maria and B. M. Alexandru, “Insights into demand-side management with big data analytics in electricity consumers’ behaviour,” Computers and Electrical Engineering, vol. 89, pp. 106902, 2021.

Z. Niu, W. Junqi, L. Xiufeng, L. Huang and P. N. Sieverts, “Understanding energy demand behaviors through spatio-temporal smart meter data analysis,” Energy, vol. 226, pp. 120493, 2021.

D. Syed, Z. Ameema, S. R. Shady and O. Bouhali, “Smart grid big data analytics: survey of technologies, techniques, and applications,” IEEE Access, vol. 9, pp. 59564-59585, 2021.

K. S. Rao and Y. V. P. Kumar, “Comprehensive modelling of renewable energy based microgrid for system level control studies,” International Journal of Renewable Energy Research, vol. 11, no. 1, pp. 223-234, 2021.

G. P. Reddy and Y. V. P. Kumar, “Retrofitted IoT based communication network with hot standby router protocol and advanced features for smart buildings,” International Journal of Renewable Energy Research, vol. 11, no. 3, pp. 1354-1369, 2021.

B. G. Fethi, R. Bayindir, S. Vadi, “Comprehensive non-intrusive load monitoring process: Device event detection, device feature extraction and device identification using KNN, random forest and decision tree,” 10th International Conference on Renewable Energy Research and Applications (ICRERA), Istanbul, Turkey, 26-29 Sep., 2021.

F. V. Scheidt, M. Hana, N. Ludwig, R. Bent, P. Staudt and W. Christof, “Data analytics in the electricity sector - a quantitative and qualitative literature review,” Energy and AI, vol. 1, pp. 100009, 2020.

K. Mladen, P. Pinson, O. Zoran, S. Grijalva, H. Tao and R. Bessa, “Big data analytics for future electricity grids,” Electric Power Systems Research, vol. 189, pp. 106788, 2020.

M. Morteza, A. Ghassemi and T. G. Aaron, “Fast big data analytics for smart meter data,” IEEE Open Journal of the Com Soc, vol. 1, pp. 1864-1871, 2020.

G. Ragini, A. R. Al-Ali, Z. A. Imran and D. K. Sajal, “Big data energy management, analytics and visualization for residential areas,” IEEE Access, vol. 8, pp. 156153-156164, 2020.

R. Bruno and S. Chren, “Smart grids data analysis: a systematic mapping study,” IEEE Transactions on Industrial Informatics, vol. 16, no. 6, pp. 3619-3639, 2020.

M. Yamauchi, O. Yuichi, M. Murata, U. Kensuke and Y. Kato, “Anomaly detection in smart home operation from user behaviors and home conditions,” IEEE Transactions on Consumer Electronics, vol. 66, no. 2, pp. 183-192, 2020.

A. E. R. Labrador and A. Taufik, “Faults in smart grid systems: monitoring, detection and classification,” Electric Power Systems Research, vol. 189, pp. 106602, 2020.

M. S. Ibrahim, W. Dong and Y. Qiang “Machine learning driven smart electric power systems: Current trends and new perspectives,” Applied Energy, vol. 272, pp. 115237, 2020.

J. C. Olivares-Rojas, E. Reyes-Archundia, A. J. Gutiérrez-Gnecchi, J. W. González-Murueta and J. Cerda-Jacobo “A multi-tier architecture for data analytics in smart metering systems,” Simulation Modelling Practice and Theory, vol. 102, pp. 102024, 2020.

C. Ilhami, B. Ramazan and S. Sagiroglu, “The effects of the smart grid system on the national grids,” 8th IEEE International Conference on Smart Grid (icSmartGrid), Paris, France, 17-19 June, 2020.

A. Faten, I. Colak, G. Ilhan and I. H. Bulbul, “Impacts of renewable energy resources in smart grid,” 8th IEEE International Conference on Smart Grid (icSmartGrid), Paris, France, 17-19 June, 2020.

B. Qolomany, A. Al-Fuqaha, G. Ajay, B. Driss, S. Alwajidi, Q. Junaid and F. C. Alvis, “Leveraging machine learning and big data for smart buildings: a comprehensive survey,” IEEE Access, vol. 7, pp. 90316-90356, 2019.

G. Maedeh, H.D. Sarineh and P. Siano, “Big data issues in smart grids: a survey,” IEEE Systems Journal, vol. 13, no. 4, pp. 4158-4168, 2019.

S. Seref, Y. Canbay and I. Colak “Solutions and suggestions for smart grid threats and vulnerabilities,” International Journal of Smart Grid, vol. 9, no. 4, 2053-2063, 2019.

B. P. Bishnu, P. Sumit, Y. Luo, M. Manish, C. Kwok, T. Reinaldo, H. Rob, S.M. Kurt, Z. Rui, P. Zhao, M. Milos, Z. Song, X. Zhang, “Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions,” IET Smart Grid, vol. 2, pp. 141-154, 2019.

W. Yi, Q. Chen, H. Tao and K. Chongqing, “Review of smart meter data snalytics: applications, methodologies, and challenges,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3125-3148, 2019.

A. Yassine, S. Singh, H. M. Shamim and G. Muhammad, G., “IoT big data analytics for smart homes with fog and cloud computing,” Future Generation Computer System, vol. 91, pp. 563–573, 2019.

A. Bani-Ahmed, N. Adel and S. Igor, “Foundational support systems of the smart grid: State of the art and future trends,” International Journal of Smart Grid, vol. 2, no. 1, pp. 1-12, 2018.

M. Ramin and J. Wang “A hierarchical framework for smart grid anomaly detection using large-scale smart meter data,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 5820-5830, 2018.

Z. Yang, T. Huang and F. E. Bompard, “Big data analytics in smart grids: a review,” Energy Informatics, vol. 1, no. 8, 2018.

Y. Mehmet and I. Colak, "Main barriers and solution proposals for communication networks and information security in smart grids,” 6th IEEE International Conference on Smart Grid (icSmartGrid), Nagasaki, Japan, 4-6 December, 2018.

M. Yesilbudak, C. Ayse, “Integration challenges and solutions for renewable energy sources, electric vehicles and demand-side initiatives in smart grids,” 7th International Conference on Renewable Energy Research and Applications (ICRERA), Paris, France, 14-17 Oct., 2018.

L. Lucy, D. Vionnet, B. Jean-Philippe and H. Jean, “Big building data – a big data platform for smart buildings,” Energy Procedia, vol. 122, pp. 589-594, 2017.

C. Tu, H. Xi, Z. Shuai, and J. Fei, “Big data issues in smart grid - a review,” Renewable and Sustainable Energy Reviews, vol. 79, pp. 1099-1107, 2017.

J. Hu and V. V. Athanasios, “Energy big data analytics and security: challenges and opportunities,” IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2423-2436, 2016.

C. Ilhami, S. Seref, G. Fulli, Y. Mehmet and C. Catalin-Felix, “A survey on the critical issues in smart grid technologies,” Renewable and Sustainable Energy Reviews, vol. 54, pp. 396–405, 2016.

S. Seref, R. Terzi, C. Yavuz and I. Colak, “Big data issues in smart grid systems,” IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, UK, Nov., 2016.

The tracebase power consumption dataset. (http://www.tracebase.org/).

Night, Twilight, and Daylight Times of a Day in Darmstadt, Germany. (https://www.timeanddate.com/astronomy/germany/darmstadt).




DOI (PDF): https://doi.org/10.20508/ijrer.v12i1.12613.g8381

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