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 …
[HTML][HTML] A critical review of comparative global historical energy consumption and future demand: The story told so far
This review presents a critical combined energy analysis of demand in
developed/develo** countries, including the load requirements of the various business …
developed/develo** countries, including the load requirements of the various business …
Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism
A Wan, Q Chang, ALB Khalil, J He - Energy, 2023 - Elsevier
This study proposes a new approach for short-term power load forecasting using a
combination of convolutional neural networks (CNN), long short-term memory (LSTM), and …
combination of convolutional neural networks (CNN), long short-term memory (LSTM), and …
A review of deep learning for renewable energy forecasting
As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …
improving the accuracy of renewable energy forecasting is critical to power system planning …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
A review of deep learning models for time series prediction
Z Han, J Zhao, H Leung, KF Ma… - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Develo** predictive models plays an important role …
been a hot research topic for decades. Develo** predictive models plays an important role …
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 …
Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability
Buildings must be energy efficient and sustainable because buildings have contributed
significantly to world energy consumption and greenhouse gas emission. Predicting energy …
significantly to world energy consumption and greenhouse gas emission. Predicting energy …
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 …
[HTML][HTML] Forecasting energy use in buildings using artificial neural networks: A review
During the past century, energy consumption and associated greenhouse gas emissions
have increased drastically due to a wide variety of factors including both technological and …
have increased drastically due to a wide variety of factors including both technological and …