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A data-driven Recommendation Tool for Sustainable Utility Service Bundles

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  • vom Scheidt, Frederik
  • Staudt, Philipp

Abstract

Managers in electric utilities face the disruption of their conventional business model of selling electricity per kilowatt-hour for invariant prices. However, the forthcoming widespread uptake of sustainable energy technologies – such as rooftop solar, batteries, heat pumps and electric vehicles – by residential customers also represents a chance for local utilities to diversify their service portfolio. To appropriately market these technologies to households, utilities need data on consumers. In this paper, we present a novel data-driven service bundle recommendation model incorporating technologies and tariffs for residential customers based on individual household data. We validate the model in a case study and quantify the utility of sharing different levels of household data. We find substantial synergies of flexible sustainable technologies and time-varying tariffs, leading to higher cost reductions for customers than tariff-switching alone that can be recommended based on easy-to-obtain data. This demonstrates a large potential for energy service bundle marketing by local utilities. The presented Machine Learning recommendation models enable more reliable recommendations than a naive benchmark. Our research thus demonstrates the potential of data-driven utility marketing strategies that focus on service bundling and the integration of customers’ energy consumption data.

Suggested Citation

  • vom Scheidt, Frederik & Staudt, Philipp, 2024. "A data-driven Recommendation Tool for Sustainable Utility Service Bundles," Applied Energy, Elsevier, vol. 353(PB).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pb:s0306261923015015
    DOI: 10.1016/j.apenergy.2023.122137
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