Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption—A systematic review
The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …
Deep learning for time series forecasting: The electric load case
Management and efficient operations in critical infrastructures such as smart grids take huge
advantage of accurate power load forecasting, which, due to its non‐linear nature, remains a …
advantage of accurate power load forecasting, which, due to its non‐linear nature, remains a …
Urban resilience and livability performance of European smart cities: A novel machine learning approach
Smart cities are centres of economic opulence and hope for standardized living.
Understanding the shades of urban resilience and livability in smart city models is of …
Understanding the shades of urban resilience and livability in smart city models is of …
Deep-learning forecasting method for electric power load via attention-based encoder-decoder with bayesian optimization
Short-term electrical load forecasting plays an important role in the safety, stability, and
sustainability of the power production and scheduling process. An accurate prediction of …
sustainability of the power production and scheduling process. An accurate prediction of …
Deep learning for load forecasting: Sequence to sequence recurrent neural networks with attention
L Sehovac, K Grolinger - Ieee Access, 2020 - ieeexplore.ieee.org
The biggest contributor to global warming is energy production and use. Moreover, a push
for electrical vehicle and other economic developments are expected to further increase …
for electrical vehicle and other economic developments are expected to further increase …
Transformer-based model for electrical load forecasting
Amongst energy-related CO 2 emissions, electricity is the largest single contributor, and with
the proliferation of electric vehicles and other developments, energy use is expected to …
the proliferation of electric vehicles and other developments, energy use is expected to …
Generating energy data for machine learning with recurrent generative adversarial networks
The smart grid employs computing and communication technologies to embed intelligence
into the power grid and, consequently, make the grid more efficient. Machine learning (ML) …
into the power grid and, consequently, make the grid more efficient. Machine learning (ML) …
A review of deep learning techniques for forecasting energy use in buildings
J Runge, R Zmeureanu - Energies, 2021 - mdpi.com
Buildings account for a significant portion of our overall energy usage and associated
greenhouse gas emissions. With the increasing concerns regarding climate change, there …
greenhouse gas emissions. With the increasing concerns regarding climate change, there …
Exploring the benefits and limitations of digital twin technology in building energy
Buildings consume a significant amount of energy throughout their lifecycle; Thus,
sustainable energy management is crucial for all buildings, and controlling energy …
sustainable energy management is crucial for all buildings, and controlling energy …
A review on the adoption of AI, BC, and IoT in sustainability research
The rise of artificial intelligence (AI), blockchain (BC), and the internet of things (IoT) has had
significant applications in the advancement of sustainability research. This review examines …
significant applications in the advancement of sustainability research. This review examines …