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The reduction of energy demand in buildings is one of the key challenges in contemporary construction. To this end, the application of structural and material partitioning solutions that provide a high level of thermal insulation and the employment of technical installations with high energy performance have become widespread. However, there are a number of other factors that can reduce energy demand. These include the optimal use of heat gains from solar radiation. An aspect that is often discussed in the literature is the overheating of buildings due to excessive heat gains from solar radiation. This article is a case study showing the impact of the orientation of a single-family passive house on its heating energy demand. The building under consideration is located in Central Europe. External climate parameters measured directly at the site during experimental examinations were used for the calculations. This paper adopts six calculation options, considering the different orientations of the glazed façade. As the simulations showed, the effect of solar radiation on the energy demand between two extreme options of glazing orientation, that is south and north-facing orientation, reached 4.7% of the annual energy demand for heating, while for the option corresponding to the actual location of the building and the option involving south-facing windows, the difference was 0.3%, respectively. [ABSTRACT FROM AUTHOR]

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of fluctuating renewable energy sources. Shifting EV charging during the day can contribute to absorbing photovoltaic (PV) production peaks and limiting the additional demand during peak periods. The EV batteries can then be discharged to the grid or home during the evening or other demand peaks. We developed a new methodology to quantify the flexibility gained from the EV-PV coupling from local mobility habits and local (or decentralized) photovoltaic production. Our approach first focuses on the geospatial modelling of mobility habits across Europe to quantify energy demand for charging. The charging demand is then distributed between residential areas, workplaces and points of interest (shopping, leisure, etc.) to model the spatiotemporal distribution of energy needs. We show and discuss a practical case in Copenhagen illustrating the impact of charging behaviour for three different scenarios. The methodology is implemented as a calculation module in the open-source online geographic tool for energy transition planning, Citiwatts. [ABSTRACT FROM AUTHOR]

Global acceptance of sustainable development and renewable energy is rising as a result of climate change's growing effect on national and local governments. The EU 2030 agenda, which aspires to give everyone access to affordable, dependable, and sustainable energy in the future, reflects this. A barrier to achieving this goal is some renewable energy sources' limited reliability. While both private citizens and governmental organizations work to produce enough renewable energy to meet their needs, it is unclear how much investment will be needed to lessen the unreliability caused by environmental factors such seasonal variations in wind speed and daylight. In this regard, a tool that helps predict the energy output of sustainable sources throughout the course of the year for a particular location can significantly help in boosting the efficiency of sustainable energy investments. In this study, we use Internet of Things (IoT) sensors, installations spread across Europe, and open data sources to build such a tool utilizing artificial neural networks. We investigate the effects of various factors on the estimation of energy production as well as the potential use of public data to predict the expected output of sustainable sources. We give users the information they need to decide what investments to make based on the necessary energy production for their particular location. Our method offers an abstraction layer that is focused on energy production rather than radiation statistics and can be taught and customized for different locations using open data, in contrast to cutting-edge alternatives. [ABSTRACT FROM AUTHOR]