Building energy prediction using artificial neural networks: A literature survey
C Lu, S Li, Z Lu - Energy and Buildings, 2022 - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …
improving energy efficiency in building energy management systems. Essentially, building …
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 …
A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data
With the development of advanced information techniques, smart energy meters have made
a considerable amount of real-time electricity consumption data available. These data …
a considerable amount of real-time electricity consumption data available. These data …
A CNN and LSTM-based multi-task learning architecture for short and medium-term electricity load forecasting
S Zhang, R Chen, J Cao, J Tan - Electric power systems research, 2023 - Elsevier
Electricity load forecasting is the forecast of power load in the future period based on
historical load and its related factors. It is of great importance for power system planning …
historical load and its related factors. It is of great importance for power system planning …
Predictive models for daylight performance of general floorplans based on CNN and GAN: A proof-of-concept study
Q He, Z Li, W Gao, H Chen, X Wu, X Cheng… - Building and …, 2021 - Elsevier
A daylight performance evaluation at the early design stage is essential for a building
morphology design and optimization, having a tremendous influence on energy …
morphology design and optimization, having a tremendous influence on energy …
Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation
Building energy prediction and management has become increasingly important in recent
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …
Agile and integrated workflow proposal for optimising energy use, solar and wind energy potential, and structural stability of high-rise buildings in early design …
Postponing performance analysis of buildings until the final stage of the design process
often leads to unexpected results, introducing errors and disrupting the architect's workflow …
often leads to unexpected results, introducing errors and disrupting the architect's workflow …
Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization
The application of Photovoltaic (PV) system in buildings is growing rapidly in response to the
need for clean energy sources and building decarbonization targets. Nonetheless …
need for clean energy sources and building decarbonization targets. Nonetheless …
Optimal battery energy storage system scheduling within renewable energy communities
In this work, a strategy for scheduling a battery energy storage system (BESS) in a
renewable energy community (REC) is proposed. RECs have been defined at EU level by …
renewable energy community (REC) is proposed. RECs have been defined at EU level by …
A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions
Short-term building energy predictions serve as one of the fundamental tasks in building
operation management. While large numbers of studies have explored the value of various …
operation management. While large numbers of studies have explored the value of various …