A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Adaptive facade network — Europe

Energy efficient buildings significantly contribute to meeting the EU climate and energy sustainability targets for 2020 as approximately one-third of all end-user energy in Europe today is consumed by space heating/cooling, ventilation and lighting of buildings. In this context, the energy performance of future building envelopes will play a key role.  The main aim of COST Action TU1403 with 120 participants from 26 European countries is to harmonise, share and disseminate technological knowledge on adaptive facades on a European level and to generate ideas for new innovative technologies and solutions

Analysis of ventilation strategies for the nearly zero energy retrofit of a day care center

The scientific literature often reports examples of educational buildings with extremely poor ventilation performance. An in-field investigation for the environmental and energy assessment of a day care center in Italy in Milano, confirmed that operable windows were not opened on days when the average daily outdoor temperature was below 15°C, seriously affecting indoor air quality and potentially affecting the wellbeing and learning process of the children. A numerical model for the dynamic energy simulation of the school building was developed to optimize the thermal insulation of opaque and transparent envelope, the solar control strategy, reducing energy needs and uses to implement a nearly zero-energy approach to the retrofit. Different ventilation strategies were therefore simulated, in order to evaluate the one(s) that best fit the deep energy retrofit of the building, including building envelope and systems. A control logic for hybrid ventilation was simulated and analyzed, with the aim to develop a strategy suited for replication and effective in ameliorating both energy performance and indoor environmental quality. Daytime and nighttime natural ventilation showed to be extremely effective in improving thermal comfort conditions, during the cooling season, performing better than mechanical ventilation