AI and Data Democratisation for Intelligent Energy Management
Abstract
:1. Introduction
1.1. Barriers to the Diffusion of AI and Data Democratisation in Energy Sector
1.2. Overarching Objective of This Research
2. Data Democracy in the Era of Data Science
2.1. Conceptualising Data Democratisation
2.2. Data Democracy in the Public Sector
2.3. Data Democracy in the Private Sector
2.4. Data and AI Democracy and the EU’s Strategy to Overcome Related Barriers
- The recent initiative to boost AI [46] to support everyday life, while creating a fertile ground for EU entrepreneurs and SMEs to create innovative AI-based services.
- The AI4EU platform [47], which has been funded by the European Union (EU) with a view to set up and manage a one-stop-shop environment where know-how resources, algorithms, solutions, and services related to AI are channelled.
3. The Growing Importance of AI and Data Democratisation
3.1. A Brief Literature Overview
3.2. Data Democracy in the Energy Sector
4. A Data Democratisation Framework for Intelligent Energy Management
- Increasing the efficiency and reliability of the electricity network.
- Optimising the management of DER assets connected to the grid.
- De-risking investments in energy efficiency and increasing the efficiency and comfort of buildings.
4.1. Data Discovery and Interoperability
4.2. Data Quality Compliance
4.3. Data Privacy and Sharing
4.4. AI-Based Library
4.5. Analytics Service Builder and Visualisation
4.6. AI Energy Analytics Services
4.6.1. Increasing the Efficiency and Reliability of the Electricity Network
- Predictive and prescriptive TSO/DSO grid-owned asset maintenance to facilitate and support grid optimal operation and/or planning, by trading off maintenance cost against accelerated asset ageing due to network overloads, and by combining and integrating grid endogenous and exogenous context-based information, such as Light Detection and Ranging (LIDAR), weather, and geographic.
- Edge-level network load and renewable energy generation prediction, by leveraging and integrating heterogeneous data from network assets, consumers and DERs smart meters, weather forecasting, geographical information, with a view to providing actionable insight to enable optimised grid operation and planning.
4.6.2. Optimising the Management of DER Assets Connected to the Grid
4.6.3. De-Risking Investments in Energy Efficiency and Increasing the Efficiency and Comfort of Buildings
4.7. Security
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Batarseh, F.A.; Yang, R. Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering, 1st ed.; Academic Press: Cambridge, MA, USA, 2020; p. 266. [Google Scholar]
- Awasthi, P.; George, J.J. A case for data democratization. In Proceedings of the Americas Conference on Information Systems (AMCIS) 2020 Proceedings, Salt Lake City, UT, USA, 10–14 August 2020; p. 23. Available online: https://aisel.aisnet.org/amcis2020/data_science_analytics_for_decision_support/data_science_analytics_for_decision_support/23 (accessed on 14 June 2021).
- Helbing, D.; Frey, B.S.; Gigerenzer, G.; Hafen, E.; Hagner, M.; Hofstetter, Y.; Van Den Hoven, J.; Zicari, R.V.; Zwitter, A. Will democracy survive big data and artificial intelligence? In Towards Digital Enlightenment; Helbing, D., Ed.; Springer: Cham, Switzerland, 2017. [Google Scholar]
- Pujari, A.K. Data Mining Techniques, 1st ed.; Universities Press: Hyderabad, India, 2001; p. 288. [Google Scholar]
- Olson, D.L.; Delen, D. Advanced Data Mining Techniques, 1st ed.; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2008; p. 180. [Google Scholar]
- Kotsiantis, S.B.; Zaharakis, I.D.; Pintelas, P.E. Machine learning: A review of classification and combining techniques. Artif. Intell. Rev. 2006, 26, 159–190. [Google Scholar] [CrossRef]
- Dey, A. Machine learning algorithms: A review. Int. J. Comput. Sci. Inf. Technol. 2016, 7, 1174–1179. [Google Scholar]
- Dutton, D.M.; Conroy, G.V. A review of machine learning. Knowl. Eng. Rev. 1997, 12, 341–367. [Google Scholar] [CrossRef]
- Brunette, E.S.; Flemmer, R.C.; Flemmer, C.L. A review of artificial intelligence. In Proceedings of the 2009 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand, 10–12 February 2009; pp. 385–392. [Google Scholar]
- Oke, S.A. A literature review on artificial intelligence. Int. J. Inf. Manag. Sci. 2008, 19, 535–570. [Google Scholar]
- Dhar, V. Data science and prediction. Commun. ACM 2013, 56, 64–73. [Google Scholar] [CrossRef]
- Provost, F.; Fawcett, T. Data science and its relationship to big data and data-driven decision making. Big Data 2013, 1, 51–59. [Google Scholar] [CrossRef]
- Waller, M.A.; Fawcett, S.E. Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. J. Bus. Logist. 2013, 34, 77–84. [Google Scholar] [CrossRef]
- Van Der Aalst, W. Data science in action. In Process Mining; Springer: Berlin/Heidelberg, Germany, 2016; pp. 3–23. [Google Scholar]
- Bhattarai, B.P.; Paudyal, S.; Luo, Y.; Mohanpurkar, M.; Cheung, K.; Tonkoski, R.; Hovsapian, R.; Myers, K.S.; Zhang, R.; Zhao, P.; et al. Big data analytics in smart grids: State-of-the-art, challenges, opportunities, and future directions. IET Smart Grid 2019, 2, 141–154. [Google Scholar] [CrossRef]
- Siozios, K.; Anagnostos, D.; Soudris, D.; Kosmatopoulos, E. IoT for Smart Grids: Design Challenges and Paradigms; Springer: Cham, Switzerland, 2019; p. 282. [Google Scholar]
- Caramizaru, A.; Uihlein, A. Energy communities: An overview of energy and social innovation. In EUR 30083 EN; Publications Office of the European Union: Luxembourg, 2020. [Google Scholar]
- Xu, Y.; Ahokangas, P.; Louis, J.N.; Pongrácz, E. Electricity market empowered by artificial intelligence: A platform approach. Energies 2019, 12, 4128. [Google Scholar] [CrossRef] [Green Version]
- Elavarasan, R.M.; Afridhis, S.; Vijayaraghavan, R.R.; Subramaniam, U.; Nurunnabi, M. SWOT analysis: A framework for comprehensive evaluation of drivers and barriers for renewable energy development in significant countries. Energy Rep. 2020, 6, 1838–1864. [Google Scholar]
- Henzelmann, T.; Hammermeister, F.; Wurm, B.; Nonnenmacher, L.; Preiss, S.; Schroer, K. Artificial Intelligence: A Smart Move for Utilities; Roland Berger: Munich, Germany, 2018. [Google Scholar]
- Davenport, T.; Loucks, J.; Schatsky, D. Bullish on the business value of cognitive. Leaders in cognitive and AI weigh in on what’s working and what’s next. In The 2017 Deloitte State of Cognitive Survey; Deloitte Development: New York, NY, USA, 2017. [Google Scholar]
- Marinakis, V.; Doukas, H.; Tsapelas, J.; Mouzakitis, S.; Sicilia, Á.; Madrazo, L.; Sgouridis, S. From big data to smart energy services: An application for intelligent energy management. Future Gener. Comput. Syst. 2020, 110, 572–586. [Google Scholar] [CrossRef]
- Desjardins, J. How Much Data Is Generated Each Day? World Economic Forum. 2019. Available online: https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-day-cf4bddf29f/ (accessed on 14 June 2021).
- Stucke, M.E. Should We Be Concerned about Data-Opolies? 2 Georgetown Law Technology Review 275 (2018), University of Tennessee Legal Studies Research Paper No. 349. Available online: https://ssrn.com/abstract=3144045 (accessed on 14 June 2021).
- McIntosh, D. We need to talk about data: How digital monopolies arise and why they have power and influence. J. Technol. Law Policy 2018, 23, 185. [Google Scholar]
- Magalhaes, G.; Roseira, C.; Manley, L. Business models for open government data. In Proceedings of the International Conference on Theory and Practice of Electronic Governance, Guimarães, Portugal, 27–30 October 2014. [Google Scholar]
- Martin, C. Barriers to the open government data agenda: Taking a multi-level perspective. Policy Internet 2014, 6, 217–240. [Google Scholar] [CrossRef]
- Janssen, K. The influence of the PSI Directive on open government data: An overview of recent developments. Gov. Inf. Q. 2011, 28, 446–456. [Google Scholar] [CrossRef]
- Data.gov. Available online: https://www.data.gov/ (accessed on 14 June 2021).
- Data.gov.in. Available online: https://data.gov.in/ (accessed on 14 June 2021).
- Data.gov.uk. Available online: https://data.gov.uk/ (accessed on 14 June 2021).
- European Commission. Digital Agenda: Turning Government Data into Gold; European Commission: Brussels, Belgium, 2011. [Google Scholar]
- Kaasenbrood, M.; Zuiderwijk, A.; Janssen, M.; de Jong, M.; Bharosa, N. Exploring the factors influencing the adoption of open government data by private organisations. Int. J. Public Adm. Digit. Age 2015, 2, 75–92. [Google Scholar]
- Ferro, E.; Osella, M. Eight business model archetypes for PSI re-use. In Open Data on the Web Workshop; Google Campus: London, UK, 2013. [Google Scholar]
- Janssen, M.; Zuiderwijk, A. Infomediary business models for connecting open data providers and users. Soc. Sci. Comput. Rev. 2014, 32, 694–711. [Google Scholar] [CrossRef]
- Zuiderwijk, A.; Janssen, M.; Poulis, K.; van de Kaa, G. Open data for competitive advantage: Insights from open data use by companies. In Proceedings of the 16th Annual International Conference on Digital Government Research, Phoenix, AZ, USA, 27–30 May 2015; pp. 79–88. [Google Scholar]
- Streeter, L.A.; Kraut, R.E.; Lucas, H.C.; Caby, L. How open data networks influence business performance and market structure. Commun. ACM 1996, 39, 62–73. [Google Scholar] [CrossRef]
- Pournaras, E.; Nikolic, J.; Omerzel, A.; Helbing, D. Engineering democratization in internet of things data analytics. In Proceedings of the2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), Taipei, Taiwan, 27–29 March 2017; pp. 994–1003. [Google Scholar]
- Pournaras, E.; Gaere, E.; Kunz, R.; Ghulam, A.N. Democratizing data analytics: Crowd-sourcing decentralized collective measurements. In Proceedings of the 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W), Umea, Sweden, 16–20 June 2019; pp. 265–266. [Google Scholar]
- Allen, B.; Agarwal, S.; Kalpathy-Cramer, J.; Dreyer, K. Democratizing AI. J. Am. Coll. Radiol. 2019, 16, 961–963. [Google Scholar] [CrossRef]
- Bagrow, J.P. Democratizing AI: Non-expert design of prediction tasks. PeerJ Comput. Sci. 2020, 6, e296. [Google Scholar] [CrossRef]
- Banifatemi, A.; Miailhe, N.; Çetin, R.B.; Cadain, A.; Lannquist, Y.; Hodes, C. Democratizing AI for humanity: A common goal. In Reflections on Artificial Intelligence for Humanity; Springer: Cham, Switzerland, 2021; pp. 228–236. [Google Scholar]
- Montes, G.A.; Goertzel, B. Distributed, decentralized, and democratized artificial intelligence. Technol. Forecast. Soc. Chang. 2019, 141, 354–358. [Google Scholar] [CrossRef]
- Moreau, E.; Vogel, C.; Barry, M. A paradigm for democratizing artificial intelligence research. In Innovations in Big Data Mining and Embedded Knowledge; Springer: Cham, Switzerland, 2019; pp. 137–166. [Google Scholar]
- Mixson, E. Make Data Accessible to Everyone with Data Democratization. Available online: https://www.aidataanalytics.network/data-democratization/articles/making-data-accessible-to-everyone-with-data-democratization (accessed on 14 June 2021).
- European Commission. White Paper on Artificial Intelligence—A European Approach to Excellence and Trust; COM(2020) 65 Final; European Commission: Brussels, Belgium, 2020. [Google Scholar]
- AI4EU—A European AI on Demand Platform and Ecosystem a European. Available online: https://www.ai4eu.eu/ (accessed on 14 June 2021).
- European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions—A European Strategy for Data; COM(2020) 66 Final; European Commission: Brussels, Belgium, 2020. [Google Scholar]
- McLaughlin, R.; Young, C. Data democratization and spatial heterogeneity in the housing market. A Shared Future: Fostering Communities of Inclusion in an Era of Inequality; Harvard Joint Center for Housing Studies: Cambridge, MA, USA, 2018; pp. 126–139. [Google Scholar]
- Grey, J. The Democratization of Data. Housing Wire. Available online: https://www.housingwire.com/articles/40946-the-democratization-of-data/ (accessed on 14 June 2021).
- Williams, D. How Big Data Will Impact Real Estate Buying, Selling and Developing. Mansion Global. Available online: https://www.mansionglobal.com/articles/how-big-data-will-impact-real-estate-buying-selling-and-developing-210771 (accessed on 14 June 2021).
- Lewis, K.; Pham, C.; Batarseh, F.A. Data openness and democratization in healthcare: An evaluation of hospital ranking methods. In Data Democracy; Academic Press: Cambridge, MA, USA, 2020; pp. 109–126. [Google Scholar]
- Kuiler, E.W.; McNeely, C.L. Knowledge formulation in the health domain: A semiotics-powered approach to data analytics and democratization. In Data Democracy; Academic Press: Cambridge, MA, USA, 2020; pp. 127–146. [Google Scholar]
- Minielly, N.; Hrincu, V.; Illes, J. Privacy challenges to the democratization of brain data. iScience 2020, 23, 101134. [Google Scholar] [CrossRef]
- Koch, T. Welcome to the revolution: COVID-19 and the democratization of spatial-temporal data. Patterns 2021, 2, 100272. [Google Scholar] [CrossRef]
- Yoder, R.T. Digitalization and Data Democratization in Offshore Drilling; Offshore Technology Conference (OTC): Houston, TX, USA, 2019. [Google Scholar]
- DiChristopher, T. Oil Firms Are Swimming in Data They Don’t Use. CNBC. Available online: https://www.cnbc.com/2015/03/05/us-energy-industry-collects-a-lot-of-operational-data-but-doesnt-use-it.html (accessed on 14 June 2021).
- Husseini, T. Big Data in Oil and Gas Operations and Other Tech Advancements: Seven Expert Opinions. Offshore Technology. Available online: https://www.offshore-technology.com/features/big-data-in-oil-and-gas-tech/ (accessed on 14 June 2021).
- Yuan, K.; O’Neil, P.; Torrejon, D. Landsat’s past paves the way for data democratization in earth science. In Data Democracy; Academic Press: Cambridge, MA, USA, 2020; pp. 147–161. [Google Scholar]
- Faghmous, J.H.; Kumar, V. A big data guide to understanding climate change: The case for theory-guided data science. Big Data 2014, 2, 155–163. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Li, Y.; Li, C. Electronic agriculture, blockchain and digital agricultural democratization: Origin, theory and application. J. Clean. Prod. 2020, 268, 122071. [Google Scholar] [CrossRef]
- Nikas, A.; Doukas, H.; Papandreou, A. A detailed overview and consistent classification of climate-economy models. In Understanding Risks and Uncertainties in Energy and Climate Policy; Doukas, H., Flamos, A., Lieu, J., Eds.; Springer: Cham, Switzerland, 2019; pp. 1–54. [Google Scholar]
- Pfenninger, S.; Hirth, L.; Schlecht, I.; Schmid, E.; Wiese, F.; Brown, T.; Wingenbach, C. Opening the black box of energy modelling: Strategies and lessons learned. Energy Strategy Rev. 2018, 19, 63–71. [Google Scholar] [CrossRef]
- Doukas, H.; Nikas, A. Decision support models in climate policy. Eur. J. Oper. Res. 2020, 280, 1–24. [Google Scholar] [CrossRef]
- Doukas, H.; Nikas, A. Involve citizens in climate-policy modelling. Nature 2021, 590, 389. [Google Scholar] [CrossRef]
- Nikas, A.; Gambhir, A.; Trutnevyte, E.; Koasidis, K.; Lund, H.; Thellufsen, J.Z.; Doukas, H. Perspective of comprehensive and comprehensible multi-model energy and climate science in Europe. Energy 2021, 215, 119153. [Google Scholar] [CrossRef]
- Huppmann, D. Open science has to go beyond open source. In Nexus—The Research Blog If IIASA; Intentional Institute for Applied Systems Analysis: Laxenburg, Austria, 2020. [Google Scholar]
- Galende-Sánchez, E.; Sorman, A.H. From consultation toward co-production in science and policy: A critical systematic review of participatory climate and energy initiatives. Energy Res. Soc. Sci. 2021, 73, 101907. [Google Scholar] [CrossRef]
- Nikas, A.; Elia, A.; Boitier, B.; Koasidis, K.; Doukas, H.; Casetti, G.; Chiodi, A. Where is the EU headed given its current climate policy? A stakeholder-driven model inter-comparison. Sci. Total. Environ. 2021, in press. [Google Scholar] [CrossRef]
- Nikas, A.; Skalidakis, S.; Sorman, A.H.; Galende-Sanchez, S.; Koasidis, K.; Serepas, F.; Doukas, H. Integrating integrated assessment modelling in support of the Paris Agreement: The I2AM PARIS platform. In Proceedings of the Twelfth International Conference on Information, Intelligence, Systems and Applications (IISA 2021), Chania, Crete, 12–14 July 2021. [Google Scholar]
- Marinakis, V.; Doukas, H. An advanced IoT-based system for intelligent energy management in buildings. Sensors 2018, 18, 610. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Marinakis, V. Big data for energy management and energy-efficient buildings. Energies 2020, 13, 1555. [Google Scholar] [CrossRef] [Green Version]
- Cichocki, A. Era of big data processing: A new approach via tensor networks and tensor decompositions. arXiv 2014, arXiv:1403.2048. [Google Scholar]
- Kontzinos, C.; Kontoulis, M.; Kapsalis, P.; Markaki, O.; Mouzakitis, S.; Manta, R.; Thireos, E. Methodology for secure storage and information exchange of medical data based on blockchain. Arch. Hell. Med. 2020, 37, 542–554. [Google Scholar]
- Kontzinos, C.; Markaki, O.; Kokkinakos, P.; Karakolis, V.; Skalidakis, S.; Psarras, J. University process optimisation through smart curriculum design and blockchain-based student accreditation. In Proceedings of the 18th International Conference on WWW/Internet, Cagliari, Italy, 7–9 November 2019; pp. 93–100. [Google Scholar]
- Pop, C.; Antal, M.; Cioara, T.; Anghel, I.; Sera, D.; Salomie, I.; Raveduto, G.; Ziu, D.; Croce, V.; Bertoncini, M. Blockchain-based scalable and tamper-evident solution for registering energy data. Sensors 2019, 19, 3033. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pop, C.; Cioara, T.; Antal, M.; Anghel, I.; Salomie, I.; Bertoncini, M. Blockchain based decentralized management of demand response programs in smart energy grids. Sensors 2018, 18, 162. [Google Scholar] [CrossRef] [Green Version]
- Jato-Espino, D.; Ruiz-Puente, C. Fostering circular economy through the analysis of existing open access industrial symbiosis databases. Sustainability 2020, 12, 952. [Google Scholar] [CrossRef] [Green Version]
- Gupta, S.; Chen, H.; Hazen, B.T.; Kaur, S.; Santibañez Gonzalez, E.D.R. Circular economy and big data analytics: A stakeholder perspective. Technol. Forecast. Soc. Chang. 2019, 144, 466–474. [Google Scholar] [CrossRef]
- Perella, M. Big Data and Circular Economy—The Revolution Will be Circular. Reuters Events. 2016. Available online: https://www.reutersevents.com/sustainability/big-data-and-circular-economy-revolution-will-be-circular (accessed on 14 June 2021).
- Marinakis, V.; Doukas, H.; Psarras, J. Energy Management 4.0. In Handbook of Research on Artificial Intelligence, Innovation and Entrepreneurship; Grigoroudis, E., Elias, G.C., Eds.; Edward Elgar Publishing: Cheltenham, UK, 2021; in press. [Google Scholar]
- Nikas, A.; Lieu, J.; Sorman, A.; Gambhir, A.; Turhan, E.; Baptista, B.V.; Doukas, H. The desirability of transitions in demand: Incorporating behavioural and societal transformations into energy modelling. Energy Res. Soc. Sci. 2020, 70, 101780. [Google Scholar] [CrossRef]
- International Data Spaces Association. Available online: https://internationaldataspaces.org/ (accessed on 14 June 2021).
- FIWARE. Available online: https://www.fiware.org/ (accessed on 14 June 2021).
- Lee, S.-K.; Kim, K.-R.; Yu, J.-H. BIM and ontology-based approach for building cost estimation. Autom. Constr. 2014, 41, 96–105. [Google Scholar] [CrossRef]
- Smart Appliances REFerence (SAREF) Ontology. Available online: https://sites.google.com/site/smartappliancesproject/ontologies/reference-ontology (accessed on 14 June 2021).
- Brick. Available online: https://brickschema.org/ (accessed on 14 June 2021).
- Project Haystack. Available online: https://project-haystack.org/ (accessed on 14 June 2021).
- European Commission. Energy Use in Buildings. Available online: https://ec.europa.eu/energy/en/eu-buildings-factsheets-topics-tree/energy-use-buildings (accessed on 14 June 2021).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Marinakis, V.; Koutsellis, T.; Nikas, A.; Doukas, H. AI and Data Democratisation for Intelligent Energy Management. Energies 2021, 14, 4341. https://doi.org/10.3390/en14144341
Marinakis V, Koutsellis T, Nikas A, Doukas H. AI and Data Democratisation for Intelligent Energy Management. Energies. 2021; 14(14):4341. https://doi.org/10.3390/en14144341
Chicago/Turabian StyleMarinakis, Vangelis, Themistoklis Koutsellis, Alexandros Nikas, and Haris Doukas. 2021. "AI and Data Democratisation for Intelligent Energy Management" Energies 14, no. 14: 4341. https://doi.org/10.3390/en14144341