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Starting in 2007, EU set energy efficiency improvement targets in sectors with high energy-saving potential such as buildings. ICT allows innovative opportunities for energy consumption forecast to integrate with new control policies such as Demand/Response and Demand Side Management to reduce energy waste. However, such technologies must overcome challenges such as the lack of accurate historic data required for predictions. This article proposes an innovative methodology supporting the energy management of HVAC systems, through Smart Building indoor air-temperature forecast. The applicability of innovative neural networks for time-series predictions is explored. These neural networks are first trained on an artificial but realistic dataset based on BIM simulations with real meteorological data. The inference phase is then carried out on a second dataset collected by IoT devices. Finally, Transfer Learning techniques are exploited to improve the performances predictions. Fanger's model is applied to validate results, showing consistent levels of accuracy and comfort. • Indoor air-temperature prediction in buildings with lack of large and reliable enough data. • Accurate predictions to forecast the energy required as input for load forecasting problems. • Comparison of SOA NNs in time-series prediction, specifically designed and optimized. • Transfer Learning to improve the NNs performances predictions and their ability to generalize. • Accurate and reliable prediction up to 36 h in the future [ABSTRACT FROM AUTHOR]

Recently, the energy saving potential from using setpoint temperatures based on adaptive comfort has been studied. This study proposes a computational approach, the Adaptive-Comfort-Control-Implementation Script (ACCIS), to extend the air-conditioning usage to adaptive comfort. ACCIS transforms PMV-based into adaptive setpoint building energy models according to both an Input Data File (IDF) and the setup specified by the user. Originally, ACCIS was an Energy Management System (EMS) script, but available functions have been extended, and ACCIS has been nested in an ease-to-use Python package called Adaptive Comfort Control Implemented Model (" accim "). A case study has been tested, whose results showed that adaptive setpoint temperatures could achieve an 83% of energy savings. However, its most powerful attribute is that it allows many simulations to be run with no limit because of both its high customisation properties and the fact that it allows the same IDF to be run with various EPW files. • Reduction of energy consumption by using adaptive setpoint temperatures • Automation of adaptive setpoint energy simulation runs with no limit • New possibilities to perform adaptive setpoint energy simulations at global scale [ABSTRACT FROM AUTHOR]