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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 …
A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data
C Fan, M Chen, X Wang, J Wang… - Frontiers in energy …, 2021 - frontiersin.org
The rapid development in data science and the increasing availability of building
operational data have provided great opportunities for develo** data-driven solutions for …
operational data have provided great opportunities for develo** data-driven solutions for …
A review of the-state-of-the-art in data-driven approaches for building energy prediction
Building energy prediction plays a vital role in develo** a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …
consumers and optimizing energy distribution plan for utilities. Common approaches for …
Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison
The prediction of global solar radiation for the regions is of great importance in terms of
giving directions of solar energy conversion systems (design, modeling, and operation) …
giving directions of solar energy conversion systems (design, modeling, and operation) …
Predicting solar radiation in the urban area: A data-driven analysis for sustainable city planning using artificial neural networking
Predicting solar radiation in cities using the Artificial Neural Network model (ANN) is a
pioneering step in transforming future-oriented city planning using solar energy. This …
pioneering step in transforming future-oriented city planning using solar energy. This …
Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …
response in power systems. Electrical loads are characterized by volatility and uncertainty …
A hybrid model for building energy consumption forecasting using long short term memory networks
Data driven building energy consumption forecasting models play a significant role in
enhancing the energy efficiency of the buildings through building energy management …
enhancing the energy efficiency of the buildings through building energy management …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
[HTML][HTML] A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis
With the advent of the era of big data, buildings have become not only energy-intensive but
also data-intensive. Data mining technologies have been widely utilized to release the …
also data-intensive. Data mining technologies have been widely utilized to release the …
Load forecasting with machine learning and deep learning methods
Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …
buildings without any structural change and is the basis for controlling and optimizing …