Real-time estimates of Swiss electricity savings using streamed smart meter data

• Published in: Applied energy 2025-01, Vol.377, p.124537, Article 124537
• Main Authors: Mari, Alessandro, Remlinger, Carl, Castello, Roberto, Obozinski, Guillaume, Quarteroni, Silvia, Heymann, Fabian, Galus, Matthias
• Format: Article
• Language: English
• Subjects: data collection; decision making; electric energy consumption; electricity; energy; energy conservation; Energy policy; Energy saving; Europe; Generalised additive models; Smart meters; socioeconomics; weather
• ISSN: 0306-2619
• DOI: 10.1016/j.apenergy.2024.124537

The gas crisis of 2022 put pressure on electricity prices in Europe, prompting the Swiss government to launch a national energy-saving campaign. To effectively quantify potential savings and guide timely decision-making, this campaign called for rigorous near-real-time modeling of changes in electricity consumption habits. The proposed approach estimates national electricity consumption at an hourly resolution across three consumer categories using thousands of streamed smart-meter load curves. These curves are aggregated to produce a national consumption estimate using scaling factors that account for differences among Swiss distributors. These factors are derived by regressing historical annual consumption against public socio-economic variables. The obtained national load curve is adjusted for the influence of weather conditions, the calendar and global trends, in order to compare different periods with a reference scenario. Such external effects are modeled with splines using Generalized Additive Models, trained on a 5-year dataset, to precisely measure each contribution on the national consumption and evaluate the consumers’ response to the saving plan. The results indicate a reduction of approximately 4.8% of the adjusted electricity consumption during winter 2022–2023, equivalent to an average monthly savings of 246 GWh, distributed across residential, service, and industrial sectors. •Swiss electricity demand is estimated from load curves and public socioeconomic data.•A 4.8% energy saving in winter 2022 is observed compared to the pre-COVID-19 period.•Model reveals differences in the savings behavior of three consumer groups over time.•The method enables real-time monitoring and savings opportunities for practitioners.•The scaling method can be easily adapted for use in different regions or countries.