| Title |
A data-driven Recommendation Tool for Sustainable Utility Service Bundles |
| ID_Doc |
72827 |
| Authors |
vom Scheidt, F; Staudt, P |
| Title |
A data-driven Recommendation Tool for Sustainable Utility Service Bundles |
| Year |
2024 |
| Published |
|
| DOI |
10.1016/j.apenergy.2023.122137 |
| 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. |
| Author Keywords |
Energy data; Machine Learning; Prosumer; Heat pumps; Energy storage; Electric vehicles; Service bundling; Electricity tariffs; Service recommendation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001102804200001 |
| WoS Category |
Energy & Fuels; Engineering, Chemical |
| Research Area |
Energy & Fuels; Engineering |
| PDF |
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