Distributed energy from photovoltaic (PV) systems are an important part of the 2000-Watt-Society and Swiss Energy Strategy 2050 targets. However, as more PV is added to the grid, it becomes more important to intelligently use on-site electricity generation. There is also a recent focus on communities as they have better load aggregation potential from multiple tenants to increase overall self-consumption. Additional potentials exists when introducing PV systems in communities from social comparisons or competitions. The first critical step is to have an effective communication platform with real-time electricity production and consumption to show resource availability and usage at the individual and community level. We have therefore developed ‘Our Energy’, a custom mobile application (app) to ‘experience’ what it would be like to have direct access to electricity from solar PV in a community setting. It is designed to facilitate demand side response for users to compare their household energy consumption and associated self-consumption from. This study uses a community based social marketing approach to support the app design, development, evaluation and deployment for a specific user group from a small Swiss city. This paper includes the results from a pilot study where the beta version of the app was tested during two weeks in March 2019 with 32 participants. The feedback helped improve the demand side response strategy along with the real-time statistics for participants to monitor self-consumption and electricity savings potential from Solar PV.

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Sustainable design requires holistic decision making already in the feasibility stage. One critical aspect of a buildingś sustainability is its operational energy consumption, but energy simulations typically are too time‐consuming for early, fast‐paced design phases. Data‐based, Machine Learning models – so called surrogates – can replace time‐consuming simulations with real‐time estimates. This paper investigates the accuracy of different surrogate models for energy performance by comparing different types of models and numbers of samples. The paper also presents an integrated dashboard for holistic decision making as an application of the developed surrogate.

The importance of an architecture adapted to its climatic context is often debated. In order to avoid future unexpected environmental behavior or failure of a building during its use, building simulation tools are used in the design and require complete and consistent weather data. However, such data are not always available for the locations where buildings are simulated, and the use of data from neighboring cities becomes usual. There are, though, several uncertainties involved in the behavior of environmental variables when the climate of large urban centers is attributed to nearby localities and areas with more significant vegetation cover, water bodies, different topography, among others. The present paper aims to present the process of preparing a weather file for the Pecém Industrial and Port Complex, located at 40 km from the capital Fortaleza, Brazil, in order to be used in simulations during the design process of buildings. The synthesis of the file was achieved through the collection and treatment of information measured in loco, the application of recommended models for the estimation of missing data, and the development of an alternative method for the estimation of a Test Reference Year of localities without weather data of several years.