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              <p>Sharing information or data between actors iscrucial for forecasting distributed energy resources. However,collaboration involving the exchange of confidential data re-quires privacy-preserving models. Current models rely solelyon fully encrypted frameworks, neglecting publicly availabledata or enforcing encryption on both private and public data.Furthermore, it is unknown whether a combination of publicand private data types is beneficial to federated learning onspatiotemporal energy applications, in particular forecasting. Inthis work, we introduce a mixed encryption setting incorporatingpublic data (e.g. weather, calendar) and private data relative toenergy measurements. In this case study of day-ahead electricconsumption of neighboring Electric Vehicle charging stations,we show that combining data increases the accuracy by up to6% in RMSE and that mixed encryption reduces the computationtime by up to 2 times compared to full encryption.</p>
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