Data from the Commission for Energy Regulation

 

Please note: ISSDA can only supply data in response to requests from EEA countries and those with an adequacy decision in place https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/adequacy-decisions_en

The Commission for Energy Regulation (CER) is the regulator for the electricity and natural gas sectors in Ireland. The CER was first set up in 1999 and works within the framework of national and EU energy policy which aim to create a single European electricity market that best meets the needs of Europe’s energy consumers.

CER Smart Metering Project

The CER initiated the Smart Metering Project in 2007 with the purpose of undertaking trials to assess the performance of Smart Meters, their impact on consumers’ energy consumption and the economic case for a wider national rollout. It is a collaborative energy industry-wide project managed by the CER and actively involving energy industry participants including the Sustainable Energy Authority of Ireland (SEAI), the Department of Communications, Energy and Natural Resources (DCENR), ESB Networks, Bord Gáis Networks, Electric Ireland, Bord Gáis Energy and other energy suppliers.

 

Electricity Customer Behaviour Trial

 

Study Number (SN): 0012-00

 

Commission for Energy Regulation (CER). (2012). CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010 [dataset]. 1st Edition. Irish Social Science Data Archive. SN: 0012-00. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/

 

The Smart Metering Electricity Customer Behaviour Trials (CBTs) took place during 2009 and 2010 with over 5,000 Irish homes and businesses participating. The purpose of the trials was to assess the impact on consumer’s electricity consumption in order to inform the cost-benefit analysis for a national rollout. Electric Ireland residential and business customers, and Bord Gáis Energy business customers, who participated in the trials had an electricity smart meter installed in their homes/premises and agreed to take part in research to help establish how smart metering can help shape energy usage behaviours across a variety of demographics, lifestyles and home sizes. The trials produced positive results, the reports for which are available on from CER along with further information on the Smart Metering Project.

The detailed data underlying the electricity customer behaviour trial results is now being made available in anonymised format in order to facilitate further research and the development of competitive products and services following the anticipated rollout of Smart Meters in Ireland. No personal or confidential information is contained in the data set, which instead gives anonymised behavioural and usage patterns.

Documentation

 

Gas Customer Behaviour Trial

 

Study Number (SN): 0013-00

 

Commission for Energy Regulation (CER). (2012). CER Smart Metering Project - Gas Customer Behaviour Trial, 2009-2010. [dataset]. 1st Edition. Irish Social Science Data Archive. SN: 0013-00. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/

 

The Smart Metering Gas Customer Behaviour Trials (CBTs) took place during 2010 and 2011 with nearly 2,000 Irish homes participating.   The purpose of the trials was to assess the impact on consumer’s gas consumption in order to inform the cost-benefit analysis for a national rollout. Bord Gáis Energy residential customers who participated in the trials had a gas smart meter installed in their homes and agreed to take part in research to help establish how smart metering can help shape energy usage behaviours across a variety of demographics, lifestyles and home sizes. The trials produced positive results, the reports for which are available from CER along with further information on the Smart Metering Programme.

The detailed data underlying the gas customer behaviour trial results is now being made available in anonymised format in order to facilitate further research and the development of competitive products and services following the anticipated rollout of Smart Meters in Ireland. No personal or confidential information is contained in the data set, which instead gives anonymised behavioural and usage patterns.

Documentation

Data format

Data for both Gas and Electricity is provided in Excel (survey data) and CSV (smart meter read data) formats.

 

LINKS

 

ACCESS INFORMATION

Accessing the data

 

Please note: ISSDA can only supply data in response to requests from EEA countries and those with an adequacy decision in place https://commission.europa.eu/law/law-topic/data-protection/international-dimension-data-protection/adequacy-decisions_en

To access the data, please complete the ISSDA Data Request Form for Research Purposes, sign it, and send it to ISSDA by email.

For teaching purposes, please complete the ISSDA Data Request Form for Teaching Purposes, and follow the procedures, as above. This covers sharing of data with students in a classroom situation. Teaching requests are approved on a once-off module/workshop basis. Subsequent occurances of the module/workshop require a new application. If students will subsequently using data for projects/assignments they must submit their own request form for Research Purposes. Please contact us if you have any queries.

Data will be disseminated on receipt of a fully completed, signed form. Incomplete or unsigned forms will be returned to the data requester for completion.

Please note that any reference to signature and date in this document can be read as meaning the typed name and date where such an application is forwarded electronically. 
 
 

Acknowledgements

Any work based in whole or part on resources provided by the ISSDA, should  acknowledge: “CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010 " or "CER Smart Metering Project - Gas Customer Behaviour Trial, 2009-2010." and also ISSDA, in the following way: “Accessed via the Irish Social Science Data Archive - www.ucd.ie/issda”.

Citation requirement

The data and its creators shall be cited in all publications and presentations for which the data have been used. The bibliographic citation may be in the form suggested by the archive or in the form required by the publication.

Bibliographical citation

Commission for Energy Regulation (CER). (2012). CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010 [dataset]. 1st Edition. Irish Social Science Data Archive. SN: 0012-00. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/

Commission for Energy Regulation (CER). (2012). CER Smart Metering Project - Gas Customer Behaviour Trial, 2009-2010. [dataset]. 1st Edition. Irish Social Science Data Archive. SN: 0013-00. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/

Notification

The user shall notify the Irish Social Science Data Archive of all publications where they have used the data.

 

If you have any queries you can read our FAQs.

 

Bibliography

 

Journal Articles

Abdulla, K., Steer, K., Wirth, A., and Halgamuge, S., “Improving the on-line control of energy storage via forecast error metric customization” Journal of Energy Storage, vol. 8, pp. 51 – 59, 2016. https://doi.org/10.1016/j.est.2016.09.005

Alamaniotis, M.; Gatsis, N. Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition. Energies 2019, 12, 2470.  https://doi.org/10.3390/en12132470

Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., “Enhancing Privacy in Smart Cities through Morphing of Anticipated Demand Utilizing Self-Elasticity and Genetic Algorithms,” Sustainable Cities and Society, Elsevier, April 2019, vol. 46, pp. (101426)1-12.

Arora, S., Taylor, J.W. 2016. Forecasting Electricity Smart Meter Data using Conditional Kernel Density Estimation. Omega, 59, 47-59 https://doi.org/10.1016/j.omega.2014.08.008

Carroll, P., Murphy, T., Hanley, M., Dempsey, D., & Dunne, J. (2018). Household Classification Using Smart Meter Data. Journal of Official Statistics, 34(1), 1-25.

Curbelo Montañez, C. A., & Hurst, W., A Machine Learning Approach for Unemployment Detection using the Smart Metering Infrastructure, IEEE Access, vol. 8, pp. 22525-22536, 2020

Hurst, W., Curbelo Montañez, C. A., Shone, N., & Al-Jumeily, D., An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities, IEEE Access Special Issue on Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations, vol. 8, pp. 7877-7898, 2020.

Hurst, W., Curbelo Montañez, C. A., & Al-Jumeily, D., Age Group Detection in Stochastic Gas Smart Meter Data using Decision Tree Learning, International Conference on Intelligent Computing, Special Session on Machine Learning and Deep Learning approaches in applied computing to support Industry for real-world problems, Nanchang,China, August 2019

Rogers, W., Carroll, P., & McDermott, J. (2019). A genetic algorithm approach to the smart grid tariff design problem. Soft Computing, 23(4), 1393-1405. https://doi.org/10.1007/s00500-017-2971-2

Hayes, B., Melatti, I., Mancini, T., Prodanovic, M.  and Tronci, E. "Residential Demand Management Using Individualized Demand Aware Price Policies," in IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1284-1294, May 2017. http://dx.doi.org/10.1109/TSG.2016.2596790

Joaquim L. Viegas J. l., Vieira S. M, Melício R, Mendes V.M.F., Sousa J. M. C. (2016) Classification of new electricity customers based on surveys and smart metering data. Energy, volume 107, 15 July 2016, Pages 804-817

Khan, Z. A.  and Jayaweera, D., "Approach for smart meter load profiling in Monte Carlo simulation applications," in IET Generation, Transmission & Distribution, vol. 11, no. 7, pp. 1856-1864, 5 11 2017. doi: 10.1049/iet-gtd.2016.2084

Mallor, f., Moler, J. A , Urmeneta, H. Simulation of household electricity consumption by using Functional Data Analysis. Journal of Simulation. https://doi.org/10.1057/s41273-017-0052-2

Rogers, W., & Carroll, P. (2016). Smart Meter Tariff Design to Minimise Wholesale Risk. Electronic Notes in Discrete Mathematics, 52, 21-28.

 

Working Papers/ Policy Briefings

Albrecht, S., Fritz, M., Strüker, J., Ziekow, H. (2017) Targeting customers for an optimized energy procurement - A Cost Segmentation Based on Smart Meter Load Profiles. Computer Science - Research and Development 32: 225. https://doi.org/10.1007/s00450-016-0303-x

William S., Gask K. (2015) Modelling sample data from smart-type electricity meters to assess potential within official statistics. Office for National Statistics. GSS Methodology Series No 40. https://www.ons.gov.uk/aboutus/whatwedo/programmesandprojects/theonsbigdataproject

 

Conference Papers

 

Abdulla, K., Steer, K., Wirth, A., Hoog, J. D., and Halgamuge, S., “Integrating data-driven forecasting and optimization to improve the operation of distributed energy storage,” in 2016 IEEE 14th International Conference on Smart City (SmartCity), pp. 1365–1372, Dec 2016. https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0193

Balaji J., Ram, H., Nair, B.B. (2017) Machine Learning Approaches to Electricity Consumption Forecasting in Automated Metering Infrastructure (AMI) Systems: An Empirical Study. In: Silhavy R., Senkerik R., Kominkova Oplatkova Z., Prokopova Z., Silhavy P. (eds) Cybernetics and Mathematics Applications in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-57264-2_26

Balaji, J., Ram, H., Nair B.B. (2016) Modeling of Consumption Data for Forecasting in Automated Metering Infrastructure (AMI) Systems. In: Silhavy R., Senkerik R., Oplatkova Z., Silhavy P., Prokopova Z. (eds) Automation Control Theory Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-319-33389-2_16

Carroll, P., Dunne, J., Hanley, M., & Murphy, T. (2013). Exploration of electricity usage data from smart meters to investigate household composition. In: Conference of European Statisticians, 25-27 September 2013, Geneva, Switzerl

Cugliari, J., Goude, Y.  and Poggi, J. M., Disaggregated electricity forecasting using wavelet-based clustering of individual consumers, 2016 IEEE International Energy Conference (ENERGYCON), Leuven, 2016, pp. 1-6. http://dx.doi.org/10.1109/ENERGYCON.2016.7514087

Cini, Andrea, Slobodan Lukovic, and Cesare Alippi. "Cluster-based aggregate load forecasting with deep neural networks." 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
 
Cini, Andrea, Ivan Marisca, and Cesare Alippi. “Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks." 2022 International Conference on Learning Representations (ICLR). 2022.

Fitzpatrick J., Carroll P., Ajwani D. (2020) Creating and Characterising Electricity Load Profiles of Residential Buildings. In: Lemaire V., Malinowski S., Bagnall A., Guyet T., Tavenard R., Ifrim G. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2020. Lecture Notes in Computer Science, vol 12588. Springer, Cham. https://doi.org/10.1007/978-3-030-65742-0_13 

Hayes, B., Gruber, J.  and Prodanovic, M., "Short-Term Load Forecasting at the local level using smart meter data," 2015 IEEE Eindhoven PowerTech, Eindhoven, 2015, pp. 1-6 http://dx.doi.org/10.1109/PTC.2015.7232358

Khan Z. A., Jayaweera D. and Gunduz H., (2016) "Smart meter data taxonomy for demand side management in smart grids," 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, 2016, pp. 1-8.

M. Alrizq and E. de Doncker, “A novel fuzzy based human behavior model for residential electricity consumption forecasting,” in Power and Energy Conference at Illinois (PECI), 2018 IEEE. IEEE, 2018, pp. 1–7. https://ieeexplore.ieee.org/document/8334984

Viegas J.L., Vieira S.M., Sousa J.M.C. (2015), Electricity demand profile prediction based on household characteristics. http://dx.doi.org/10.1109/EEM.2015.7216746

Viegas J.L., Vieira S.M., Sousa J.M.C. (2015), Fuzzy clustering and prediction of electricity demand based on household characteristics. http://dx.doi.org/10.2991/ifsa-eusflat-15.2015.147

Viegas J.L., Vieira S.M., Sousa J.M.C. (2016) Mining Consumer Characteristics from Smart Metering Data through Fuzzy Modelling. In: Carvalho J., Lesot MJ., Kaymak U., Vieira S., Bouchon-Meunier B., Yager R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham http://dx.doi.org/10.1007/978-3-319-40596-4_47

Voss, Marcus, Brijnesh Jain, and Sahin Albayrak. Subgradient methods for averaging household load profiles under local permutations. 2019 IEEE Milan PowerTech. IEEE, 2019.

 Voss, Marcus. Permutation-Based Residential Short-term Load Forecasting in the Context of Energy Management Optimization Objectives." Proceedings of the Eleventh ACM International Conference on Future Energy Systems. 2020.

 

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