Automated energy services to SMEs

Energy saving efforts have traditionally had difficult conditions in the SME segment (stores, smaller offices and small businesses), as the size of end-user consumption does not, in a commercial way, allow for customer visits and larger analysis work. In this project, a model tool was developed that can systematically and autonomously identify and classify energy saving potentials by combining electricity consumption data available from Energinet's "DataHub" with information about the type of business, the size of the heated area and the ambient temperatures. The tool was built by algorithms that solely analyzed the company's electricity consumption, but also compared the company with the rest of the industry (benchmarking). Through the tool, savings potentials were identified in especially the participating supermarkets, as these stores already have a high electricity consumption, and even extremely large variations in the size and profile of electricity consumption. A rough scaling up of the results from the supermarkets registered to the project, to a national level, gave an energy saving potential of 76 GWh/year - 244 GWh/year, corresponding to 0.3% - 0.8% of Denmark's total electricity consumption. In the other industry segments (clothing stores and construction markets) that participated in the project, a potential for energy improvements was also identified, and there was considerable variation in consumption between the businesses. This was mainly due to the fact that some stores had LED lighting while others still had fluorescent lamps or halogen bulbs. The project with automatic data recognition and information feedback has shown good potential for wider use in the future in order to more easily identify energy improvements. During the test phase, it was shown that many different potentials of improvement could be identified through the analysis tool and that the diagnoses in general matched well with the findings that were mapped when selected stores were visited. However, the accuracy of the diagnoses in the tool was partly limited by the fact that the electricity consumption data was only on an hourly basis and partly that the amount of companies visited during the test phase was limited. A better data resolution could enable more specific diagnoses to be developed, so that instead of simply saying that the daily consumption was high, the system could also say with greater precision why the daily consumption was high. A business model with a 3rd party involvement is immediately difficult to see, due to lack of financial incentive and the challenges of receiving permission to use energy consumption data. A business model based on automatic data recognition and benchmarking and driven by companies with access to electricity consumption data, e.g. energy companies, as a service to their customers, are obvious. Unlike a 3rd party company, energy companies have the opportunity for a larger-scale implementation, since in connection with the conclusion of the electricity trade agreement with their customers, they have the opportunity to add that the customer also gives consent that the company can use their consumption data for analysis and contact the customer with suggestions for improvements.