A REVIEW ON ELECTRICAL ENERGY DATASET: MEASUREMENT, FEATURES, AND APPLICATIONS
- Authors
-
-
Dahunsi, F. M.,
Federal University of Technology, Akure, Nigeria
-
Olawumi, A. O
Federal University of Technology, Akure, Nigeria
-
Sarumi, O. A
Federal University of Technology, Akure, Nigeria
-
Ponnle, A. A
Federal University of Technology, Akure, Nigeria
-
- Keywords:
- Energy datasets, load profiling, load forecasting, smart meters, machine learning
- Abstract
-
Electrical energy datasets provide information about industrial and residential energy use. They contain parameters such as voltage, current, real power, reactive power, apparent power, and energy which provide vital information about the electrical power supplied by the utility provider and energy consumed by the consumers. Utility companies and researchers collect energy data; data collected by the former via smart meters are mainly for billing purposes. The energy data collected by utilities is further applied in some energy management practices, such as load profiling, forecasting, and analysis. Researchers have also shown keen interest in acquiring and applying energy data to develop algorithms to aid various energy management practices. Different energy-related parameters acquired depend on the purpose in view; thus, the hardware and software architectures adopted during the data acquisition differ. This paper reviewed energy datasets to identify the various components and technologies employed during data acquisition across existing datasets. Furthermore, significant applications of energy data, such as load profiling, load forecasting, and energy theft, were discussed. These include techniques widely utilized, challenges faced during energy dataset usage, and various ways to mitigate the challenges.
- Author Biographies
- References
-
Aurangzeb, K., and Alhussein, M. (2020). Deep Learning Framework for Short Term Power Load Forecasting, a Case Study of Individual Household Energy Customers. 2019 International Conference on Advances in the Emerging Computing Technologies, AECT 2019.
Bansal, A., Rompikuntla, S. K., Gopinadhan, J., Kaur, A., and Kazi, Z. A. (2015). Energy Consumption Forecasting for Smart Meters. December 2015.
Beliaeva, N., Petrochenkov, A., and Bade, K. (2013). Data Set Analysis of Electric Power Consumption. European Researcher, 61(10–2), 2482–2487.
Bernard, T. (2018). Non-Intrusive Load Monitoring (NILM): combining Multiple Distinct Electrical Features and Unsupervised Machine Learning Techniques. https://duepublico2.uni-due.de/rsc/thumbnail/duepublico_mods_00046575.png
BP. (2021). Statistical Review of World Energy globally Consistent Data On World Energy Markets. And Authoritative Publications in The Field Of Energy. BP Energy Outlook, 70, 8–20.
Buzau, M. M., Tejedor-Aguilera, J., Cruz-Romero, P., and Gomez-Exposito, A. (2019). Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning. IEEE Transactions on Smart Grid, 10(3), 2661–2670.
Carrie Armel, K., Gupta, A., Shrimali, G., and Albert, A. (2013). Is Disaggregation The Holy Grail of Energy Efficiency? The Case Of Electricity. Energy Policy, 52, 213–234.
Chicco, G. (2012). Overview and Performance Assessment of The Clustering Methods For Electrical Load Pattern Grouping. Energy, 42(1), 68–80.
Dahunsi, F. M., Olawumi, A. E., Ale, D. T., and Sarumi, O. A. (2021). A Systematic Review Of Data Pre-Processing Methods and Unsupervised Mining Methods Used in Profiling Smart Meter Data. AIMS Electronics and Electrical Engineering, 5(4), 284–314.
Danilo, B. (2015). Intrusive and Non-Intrusive Load Monitoring (A Survey) Approach, Learning. Latin American Journal of Computing Lajc, 2(1), 45–53.
Efthymiou, C., and Kalogridis, G. (2010). Smart Grid Privacy via Anonymization of Smart Metering Data. 238–243.
Eibl, G., and Engel, D. (2015). Influence of Data Granularity on Smart Meter Privacy. IEEE Transactions on Smart Grid, 6(2), 930–939.
Govindarajan, R., Meikandasivam, S., and Vijayakumar, D. (2019). Cloud Computing Based Smart Energy Monitoring System. International Journal of Scientific and Technology Research, 8(10), 886–890.
Granell, R., Axon, C. J., and Wallom, D. C. H. (2014). Impacts of Raw Data Temporal Resolution Using Selected Clustering Methods on Residential Electricity Load Profiles. 1–8.
Grolinger, K., Capretz, M. A. M., and Seewald, L. (2016). Energy consumption Prediction With Big Data: Balancing Prediction Accuracy and Computational Resources. Proceedings - 2016 IEEE International Congress on Big Data, BigData Congress 2016, 90, 157–164.
Guo, Y. C., Niu, D. X., and Chen, Y. X. (2006). Support Vector Machine Model in Electricity Load Forecasting. Proceedings of the 2006 International Conference on Machine Learning and Cybernetics, 2006(August), 2892–2896.
Gupta, V. (2017). An Overview of Different Types of Load Forecasting Methods and the Factors Affecting the Load Forecasting. International Journal for Research in Applied Science and Engineering Technology, V(IV), 729–733.
Haq, A. U., and Jacobsen, H.-A. (2016). A Step Towards Advanced Metering for the Smart Grid: A Survey of Energy Monitors. 1–8.
Javeri, I. Y., Toutiaee, M., Arpinar, I. B., Miller, T. W., and Miller, J. A. (2021). Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML. 1–8. http://arxiv.org/abs/2103.01992
Javier Campillo, Fredrik Wallin, Daniel Torstensson, I. V. (2012). Energy Demand Model Design for Forecasting Electricity Consumption and Simulating Demand Response Scenarios in Sweden. International Conference on Applied Energy, Suzhou, China.
Jiang, R., Lu, R., Wang, Y., Luo, J., Shen, C., and Shen, X. (2014). Energy-theft Detection Issues For Advanced Metering Infrastructure in Smart Grid. Tsinghua Science and Technology, 19(2), 105–120.
Jiang, Z., Lin, R., Yang, F., Liu, Z., and Zhang, Q. (2017). Comparing Electricity Consumer Categories Based on Load Pattern Clustering With Their Natural Types. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10393 LNCS(August), 658–667.
Kahl, M., Haq, A. U., Kriechbaumer, T., and Jacobsen, H. (2016). WHITED - A Worldwide Household and Industry Transient Energy Data Set. 3rd International Workshop on Non-Intrusive Load Monitoring (NILM2016), April, 1–4.
Kelly, J., and Knottenbelt, W. (2015). The UK-DALE Dataset, Domestic Appliance-Level Electricity Demand and Whole-House Demand From Five UK Homes. Nature, 2, 1–14.
Kim, J., Le, T. T. H., and Kim, H. (2017). Non-intrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature. Computational Intelligence and Neuroscience, 2017, 1-22.
Klemenjak, C., Reinhardt, A., Pereira, L., Makonin, S., Bergés, M., and Elmenreich, W. (2019). Electricity Consumption Data Sets: Pitfalls and Opportunities. BuildSys 2019 - Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November, 159–162.
Koivisto, M., Heine, P., Mellin, I., and Lehtonen, M. (2013). Clustering of Connection Points and Load Modeling in Distribution Systems. IEEE Transactions on Power Systems, 28(2), 1255–1265.
Kolter, J. Z., and Johnson, M. J. (2011). REDD : A Public Data Set for Energy Disaggregation Research. SustKDD Workshop, xxxxx(1), 1–6. http://users.cis.fiu.edu/~lzhen001/activities/KDD2011 Program/workshops/WKS10/doc/SustKDD3.pdf
Lu, N., Du, P., Guo, X., and Greitzer, F. L. (2012). Smart Meter Data Analysis. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, 1–6.
Lua, S. W., Teng, J. H., Chan, S. Y., and Hwang, L. C. (2009). Development of a Smart Power Meter for AMI Based on ZigBee Communication. Proceedings of the International Conference on Power Electronics and Drive Systems, 661–665.
Makonin, S., Popowich, F., Bartram, L., Gill, B., and Baji?, I. V. (2013). AMPds: A public Dataset for Load Disaggregation and Eco-Feedback Research. 2013 IEEE Electrical Power and Energy Conference, EPEC 2013, Section III.
Makonin, S., Wang, Z. J., and Tumpach, C. (2018). RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis. Data, 3(1), 1–9.
Marinakis, V. (2020). Big data for energy management and energy-efficient buildings. Energies, 13(7), 1555-1573
Meshram, R., Deorankar, A. V, and Chatur, D. P. N. (2012). Load Pattern Analysis of Electricity Customers based on Clustering Algorithm. International Journal Of Computer Science And Technology, 3, 702–705.
Monacchi, A., Egarter, D., Elmenreich, W., D’Alessandro, S., and Tonello, A. M. (2015). GREEND: An energy consumption dataset of households in Italy and Austria. 2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014, 1, 511–516.
Palacios-Garcia, E. J., Rodriguez-Diaz, E., Anvari-Moghaddam, A., Savaghebi, M., Vasquez, J. C., Guerrero, J. M., and Moreno-Munoz, A. (2017). Using Smart Meters Data For Energy Management Operations and Power Quality Monitoring in a Microgrid. IEEE International Symposium on Industrial Electronics, June, 1725–1731.
Pereira, L., Quintal, F., Gonçalves, R., and Nunes, N. J. (2014). SustData: A Public Dataset for ICT4S Electric Energy Research. ICT for Sustainability 2014, ICT4S 2014, July, 359–368.
Pereira, L., Velosa, N., and Pereira, M. (2022). A Data Model and File Format to Represent and Store High Frequency Energy Monitoring and Disaggregation Datasets. Scientific Reports, 12(1), 1–13.
Picon, T., Meziane, M. N., Ravier, P., Lamarque, G., Novello, C., Bunetel, J.-C. Le, and Raingeaud, Y. (2016). COOLL: Controlled On/Off Loads Library, a Public Dataset of High-Sampled Electrical Signals for Appliance Identification. 3, 1–5.
Pitì, A., Verticale, G., Rottondi, C., Capone, A., and Lo Schiavo, L. (2017). The Role of Smart Meters in Enabling Real-Time Energy Services For Households: The Italian case. Energies, 10(2), 199-224
Quilumba, F. L., Lee, W. J., Huang, H., Wang, D. Y., and Szabados, R. (2014). An Overview of AMI Data Pre-Processing to Enhance the Performance of Load Forecasting. 2014 IEEE Industry Application Society Annual Meeting, IAS 2014, 1–7.
Ramos, S., Soares, J., Vale, Z., and Ramos, S. (2013). Short-term Load Forecasting Based on Load Profiling. 2013 IEEE Power and Energy Society General Meeting held on 21-25 July 2013, Vancouver, British Columbia, Canada.
Rodrigues, E. M. G., Godina, R., Shafie-Khah, M., and Catalão, J. P. S. (2017). Experimental Results on a Wireless Wattmeter Device for the Integration in Home Energy Management Systems. Energies, 10(3), 398 - 416
Sahoo, S., Nikovski, D., Muso, T., and Tsuru, K. (2015). Electricity Theft Detection Using Smart Meter Data. 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015, Columbia, USA.
Salehkalaibar, S., Aminifar, F., and Shahidehpour, M. (2019). Hypothesis Testing for Privacy of Smart Meters With Side Information. IEEE Transactions on Smart Grid, 10(2), 2059–2067.
Sankar, L., Raj Rajagopalan, S., Mohajer, S., and Vincent Poor, H. (2013). Smart Meter Privacy: a Theoretical Framework. IEEE Transactions on Smart Grid, 4(2), 837–846.
Sayed, S., Hussain, T., Gastli, A., and Benammar, M. (2019). Design and Realization of an Open-Source And Modular Smart Meter. Energy Science and Engineering, 7(4), 1405–1422.
Shin, C., Lee, E., Han, J., Yim, J., Rhee, W., and Lee, H. (2019). The Enertalk dataset, 15 Hz Electricity Consumption Data From 22 Houses in Korea. Scientific Data, 6(1 1), 1–13.
Shin, C., Rho, S., Lee, H., and Rhee, W. (2019). Data requirements for applying machine learning to energy disaggregation. Energies, 12(9), 1696 - 1715.
Singh, A. K., Ibraheem, Khatoon, S., Muazzam, M., and Chaturvedi, D. K. (2012). Load Forecasting Techniques and Methodologies: A review. ICPCES 2012 - 2012 2nd International Conference on Power, Control and Embedded Systems, Uttar Pradesh, India.
Solangi, K. H., Islam, M. R., Saidur, R., Rahim, N. A., and Fayaz, H. (2011). A Review on Global Solar Energy Policy. Renewable and Sustainable Energy Reviews, 15(4), 2149–2163.
Tan, O., Gunduz, D., and Poor, H. V. (2013). Increasing Smart Meter Privacy Through Energy Harvesting and Storage Devices. IEEE Journal on Selected Areas in Communications, 31(7), 1331–1341.
Tjaden, T., Bergner, J., Weniger, J., and Quaschning, V. (2015). Representative Electrical Load Profiles Of Residential Buildings In Germany With a Temporal Resolution of One Second. Dataset, HTW Berlin - University of Applied Sciences Research, November, 1–7.
Wang, M. C., Tsai, C. F., and Lin, W. C. (2021). Towards Missing Electric Power Data Imputation for Energy Management Systems. Expert Systems with Applications, Expert Systems with Applications 174(1): 114743, 1-20
Wang, Yi, Chen, Q., Hong, T., and Kang, C. (2019). Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Transactions on Smart Grid, 10(3), 3125–3148.
Wang, Yi, Chen, Q., Kang, C., Zhang, M., Wang, K., and Zhao, Y. (2015). Load Profiling and Its Application to Demand Response: A Review. 20(2), 117–129.
Wang, Yikuai, Qiu, H., Tu, Y., Liu, Q., Ding, Y., and Wang, W. (2018). A Review of Smart Metering for Future Chinese Grids. Energy Procedia, 152, 1194–1199.
Yip, S. C., Wong, K. S., Hew, W. P., Gan, M. T., Phan, R. C. W., and Tan, S. W. (2017). Detection of Energy Theft and Defective Smart Meters in Smart Grids Using Linear Regression. International Journal of Electrical Power and Energy Systems, 91, 230–240.
Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22–32.
Zeifman, M., and Roth, K. (2011). Non-intrusive Appliance Load Monitoring: Review and outlook. IEEE Transactions on Consumer Electronics, 57(1), 76–84.
Zheng, S., Zhong, Q., Peng, L., and Chai, X. (2018). A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks. Mathematical Problems in Engineering, 2018, 1-16
Zhou, K. Le, Yang, S. L., and Shen, C. (2013). A Review of Electric Load Classification in Smart Grid Environment. Renewable and Sustainable Energy Reviews, 24, 103–110.
Zoha, A., Gluhak, A., Imran, M. A., and Rajasegarar, S. (2012). Non-intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors (Switzerland), 12(12), 16838–16866.
- Downloads
- Published
- 2023-05-31
- Section
- Articles
- License
-
Copyright (c) 2023 FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright
With the submission of a manuscript, the corresponding author confirms that the manuscript is not under consideration by another journal. With the acceptance of a manuscript, the Journal reserves the exclusive right of publication and dissemination of the information contained in the article. The veracity of the paper and all the claims therein is solely the opinion of the authors not the journal.
How to Cite
Similar Articles
- Raheem, W. A., , Omiyale, A. D, Odeyinka, O. F. , Folorunso, C, A COMPARATIVE ANALYSIS OF PREDICTIVE MODELS FOR HOUSEHOLD ENERGY CONSUMPTION IN LAGOS , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 19 No. 2 (2025): FUTA Journal of Engineering and Engineering Technology
- Agbo, E.R, Olajide, I.A, Itodo, E.S, Faleye, O.P, MACHINE LEARNING FOR ANOMALY DETECTION IN SMART GRID ENERGY CONSUMPTION: A ONE-CLASS SVM APPROACH , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 19 No. 1 (2025): FUTA Journal of Engineering and Engineering Technology
- Uwadia, O.A., Dahunsi, F.M., SOIL NUTRIENT PREDICTION AND CROP PREDICTION RECOMMENDATION SYSTEMS USING IOT AND AI TECHNIQUES: CURRENT TRENDS AND CHALLENGES , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 20 No. Special (2026): FUTA JEET: Special Issue on Innovative Solutions for Sustainable Living and Environmental Challenges: Engineering Perspectives
- O. M. Adebajo, , Prof. Oluyemi-Ayibiowu, B. D. , K. E. Falola, , ASSESSMENT OF SELECTED MACHINE LEARNING SYSTEMS IN PREDICTING THE RESILIENT MODULUS OF COHESIVE SOILS , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 19 No. 2 (2025): FUTA Journal of Engineering and Engineering Technology
- E O Olutomilola, A Omoaka, THEORETICAL DESIGN OF A PLANTAIN PEELING MACHINE , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 12 No. 2 (2018): FUTA Journal of Engineering and Engineering Technology
- Akinola, A. O, Olusola, E. O, Olundegun, S. A1, DEVELOPMENT OF A PULVERIZING MACHINE FOR RAPHIA PALM SEEDS , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 19 No. 1 (2025): FUTA Journal of Engineering and Engineering Technology
- Ikem, I.A., Akintunde, M. A, Titiladunayo, I. F, Awatt.E, INVESTIGATING THE THERMODYNAMIC CHARACTERISTICS OF ENERGY SAVING REFRIGERATING SYSTEM WITH COLD ACCUMULATOR , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 19 No. 2 (2025): FUTA Journal of Engineering and Engineering Technology
- Malomo, B.O., CRASHWORTHINESS AND ENERGY ABSORPTION ANALYSIS OF MESOCARP COIR-FIBRE/EPOXY RESIN-REINFORCED GLASS FIBER HYBRID COMPOSITE LAMINATE , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 17 No. 2 (2023): FUTA Journal of Engineering and Engineering Technology
- C. O. Ijagbemi, D. T. Oloruntoba, A. O. Adeoye, Development of a Bioplastic Film for Food Packaging , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 8 No. 1 (2014): FUTA Journal of Engineering and Engineering Technology
- R. K. Apalowo, J. A. Kehinde, E. O. Oyeleke, O. J. Owoloja, S. O. Famuyiwa, MACHINE LEARNING OPTIMIZATION OF UNDERFILL FLOW TIME IN FLIP-CHIP ENCAPSULATION OF BGA ASSEMBLIES , FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY: Vol. 20 No. 1 (2026): FUTA Journal of Engineering and Engineering Technology
You may also start an advanced similarity search for this article.
