Summary
The incessant growing of the world's energy consumption and associated greenhouse gases emissions have created tremendous problems to be solved by today's and future generations. As the building sector is one of the biggest energy consumers, reducing its energy consumption is now mandatory. Being able to conceive and built efficient buildings, to effectively manage and operate them, and to rapidly renovate the existing building stock is a challenging task. Neural networks models open new possibilities to address this problem. This paper offers a comprehensive review of the studies that use neural networks for energy‐related applications in the building sector focusing on their application and on the technical characteristics of the network (ie, learning algorithm, number of layers, number of neurons, inputs and output variables, and performance criteria). On the basis of this review, limitations concerning the use of neural networks in the building sector along with existing research gaps and future research directions are identified.
The case study is the HIKARI project, the first net zero energy city block in Europe, gathering offices, housing and shops. The three mixed-use buildings composing the 12 000 m² block pool their energy through shared innovative energy production systems coupled with large capacity energy storage units. HIKARI is fully monitored with more than 10 000 measurement points collected for several years of operating conditions. Through the identification and analysis of an energy consumption drift caused by the malfunctioning of a particular system, we demonstrate the importance of adopting a global approach when conceiving a building, especially if low energy targets are to be reached. The analysis of the monitoring results also highlights the value of detailed monitoring for identifying malfunctions, for assessing the relevance of the technical choices made during the design phase, and as an essential tool for achieving the objective of energy performance under operating conditions.
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