[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
Data-driven building energy modelling–An analysis of the potential for generalisation through interpretable machine learning
Data-driven building energy modelling techniques have proven to be effective in multiple
applications. However, the debate around the possibility of generalisation is open …
applications. However, the debate around the possibility of generalisation is open …
Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives
Energy flexibility, through short-term demand-side management (DSM) and energy storage
technologies, is now seen as a major key to balancing the fluctuating supply in different …
technologies, is now seen as a major key to balancing the fluctuating supply in different …
[HTML][HTML] Free and open source urbanism: Software for urban planning practice
W Yap, P Janssen, F Biljecki - Computers, Environment and Urban Systems, 2022 - Elsevier
Free and open source tools present numerous opportunities to support current urban
planning practice. However, their overview is fragmented, and the uptake among planning …
planning practice. However, their overview is fragmented, and the uptake among planning …
A spatial-temporal layer-wise relevance propagation method for improving interpretability and prediction accuracy of LSTM building energy prediction
G Li, F Li, C Xu, X Fang - Energy and Buildings, 2022 - Elsevier
At present, data-driven methods have achieved satisfactory results in building energy
consumption prediction, especially deep learning models such as long short-term memory …
consumption prediction, especially deep learning models such as long short-term memory …
Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach
The data-driven method is a promising way to predict the energy consumption of buildings,
however suffering from the data shortage problem in various scenarios. Even though …
however suffering from the data shortage problem in various scenarios. Even though …
Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions
Traditional building energy prediction (BEP) methods usually solve time-series prediction
problems using either recursive strategy or direct strategy, which may ignore time …
problems using either recursive strategy or direct strategy, which may ignore time …
Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale
Buildings account for over a third of end energy demand in many countries worldwide.
Modelling this demand accurately marks the first step in producing forecasts that can help …
Modelling this demand accurately marks the first step in producing forecasts that can help …
[PDF][PDF] Does fiscal policy matter?
AS Blinder, RM Solow - Journal of public economics, 1973 - princeton.edu
Perhaps the most fundamental achievement of the Keynesian revolution was the re-
orientation of the way economists view the influence of government activity on the private …
orientation of the way economists view the influence of government activity on the private …
A systematic review of building electricity use profile models
The building sector contributes significantly to overall energy consumption and carbon
emissions. Improving renewable energy utilization in buildings is of considerable …
emissions. Improving renewable energy utilization in buildings is of considerable …