PUBLICATIONS

Newsletters

Newsletter #1: Introducing the Eco-Bot project

Project Public Deliverables

CodeDeliverable Title
D1.3 Data Management Plan
D1.4 Societal impact report – First
D1.5Societal impact report – Final Report
D2.1Definition of the Use Cases and Requirements
D2.2Taxonomy of energy efficiency models
D2.3Mapping consumers needs to the taxonomy model
D3.1Report on findings from consultations and online-survey
D3.3Metrics to validate Eco-Bot engagement actions and proposed measures
D5.1Pilot Plans and Report for the demo preparations
D5.5Validation Results including Lessons Learned and societal impact
D6.1Dissemination strategy and action plan Draft Version
D6.2Dissemination strategy and action plan
D6.3Report on the Eco-Bot dissemination activities (Version 1)
D6.4Report on the Eco-Bot dissemination activities (Version 2)
D6.5Digital management of the project and content production
D6.7Eco-Bot Video (Version 1)
D6.8Eco-Bot Video (Version 2)
D6.9Report on the liaison and cluster activities with the other projects (Version 1)
D6.10Report on the liaison and cluster activities with the other projects (Version 2)
D6.11Report on the liaison and cluster activities with the other projects (Version 3)
D6.12Report on the Eco-Bot dissemination activities (Version 3)
D7.5Market Environment Study
D7.6Workshop Documentation
D7.7Eco-Bot Roadmap

Project Scientific Publications

Afrasiabi, M., Mohammadi, M., Rastegar, M., Stankovic, L., Afrasiabi, S., & Khazaei, M. (2020). Deep-based conditional probability density function forecasting of residential loads. IEEE Transactions on Smart Grid11(4), 3746-3757. [8988175].  [https://doi.org/10.1109/TSG.2020.2972513 [https://pureportal.strath.ac.uk/en/publications/deep-based-conditional-probability-density-function-forecasting-o]

Khazaei, M., Stankovic, L., & Stankovic, V. (2020). Evaluation of low-complexity supervised and unsupervised NILM methods and pre-processing for detection of multistate white goods. 5th International Workshop on Non Intrusive Load Monitoring, Yokohama, Japan. In The 5th International Workshop on Non-Intrusive Load Monitoring (NILM’ 20). November 18, 2020, Virtual Event, Japan. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3427771.3427850 [https://pureportal.strath.ac.uk/en/publications/evaluation-of-low-complexity-supervised-and-unsupervised-nilm-met ]

Murray, D., Stankovic, L., & Stankovic, V. (2020). Explainable NILM Networks. 5th International Workshop on Non Intrusive Load Monitoring, Yokohama, Japan. In The 5th International Workshop on Non-Intrusive Load Monitoring (NILM’20), November 18, 2020, Virtual Event, Japan. ACM, New York, NY, USA, 6 pages.[https://doi.org/10.1145/3427771.3427855 [https://pureportal.strath.ac.uk/en/publications/explainable-nilm-networks ]

Zhao, B., Ye, M., Stankovic, L., & Stankovic, V. (2020). Non-intrusive load disaggregation solutions for very low-rate smart meter data. Applied Energy268, [114949].  [https://doi.org/10.1016/j.apenergy.2020.114949 [https://pureportal.strath.ac.uk/en/publications/non-intrusive-load-disaggregation-solutions-for-very-low-rate-sma]

Stankovic, L., & Stankovic, V. (2020, May 11). The risks and benefits of AI smart meters. Apolitical London. May 2020. [https://apolitical.co/en/solution_article/the-risks-and-benefits-of-ai-smart-meters]

Khazaei, M., Stankovic, L., & Stankovic, V. (2019). Trends and challenges in smart metering analytics. In D. Sharma (Ed.), Review of Business and Technology Research (RBTR) (2 ed., Vol. 16, pp. 111-117). (Review of Business and Technology Research; Vol. 16, No. 2). [https://pureportal.strath.ac.uk/en/publications/trends-and-challenges-in-smart-metering-analytics]

J. Trzęsiok, S. Słupik, “The identification and analysis of the factors affecting energy consumer behaviour” Research Papers of Wrocław University of Economics, 2019 vol. 63 nr 6, DOI: 10.15611/pn.2019.6.09

D. Murray, L. Stankovic, V. Stankovic, S. Lulic and S. Sladojevic, “Transferability of neural networks approaches for low-rate energy disaggregation”, Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, UK.

[​https://pureportal.strath.ac.uk/en/publications/transferability-of-neural-networks-approaches-for-low-rate-energy]

Stephanos Camarinopoulos, Theodora Karali, Ulrich Hussels “Eco-Bot Presentation – Chat-Bot for Advising Users on Individual Energy Efficiency Models”, Tagungsband UIS 2018

B. Zhao, L. Stankovic and V. Stankovic, “Electricity usage profile disaggregation of hourly smart meter data”, 4th Int. Workshop on Non-Intrusive Load Monitoring, July 2018, Austin, TX USA. [https://pureportal.strath.ac.uk/en/publications/electricity-usage-profile-disaggregation-of-hourly-smart-meter-da]

L. Stankovic, K. He, V. Stankovic, S. Lulic and S. Sladojevic, “Online accuracy estimation and improvement of event-based NILM algorithms without resorting to submetered individual loads,” EU NILM Workshop, Nov 2017, London, UK. [​https://pureportal.strath.ac.uk/en/publications/online-accuracy-estimation-and-improvement-of-event-based-nilm-al]

Marketing Material

Eco-Bot Project Poster

Eco-Bot Project flyer

German Pilot flyer (in German)

Spanish Pilot Flyer (in Catalan)

Eco-Bot logo (white – png)

Eco-Bot logo (green – png)