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 …
Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning
Optimising HVAC operations towards human wellness and energy efficiency is a major
challenge for smart facilities management, especially amid COVID situations. Although IoT …
challenge for smart facilities management, especially amid COVID situations. Although IoT …
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 …
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
Accurate and reliable solar irradiance forecasting can bring significant benefits for managing
electricity generation and distributing modern smart grid. However, the characteristics of …
electricity generation and distributing modern smart grid. However, the characteristics of …
Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network
Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …
importance for energy management, infrastructure planning, and budgeting. In recent years …
Modeling and forecasting building energy consumption: A review of data-driven techniques
Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …
energy efficiency problems and take up current challenges of human comfort, urbanization …
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 …
Electrical load forecasting using LSTM, GRU, and RNN algorithms
Forecasting the electrical load is essential in power system design and growth. It is critical
from both a technical and a financial standpoint as it improves the power system …
from both a technical and a financial standpoint as it improves the power system …
Attention-based interpretable neural network for building cooling load prediction
Abstract Machine learning has gained increasing popularity in building energy management
due to its powerful capability and flexibility in model development as well as the rich data …
due to its powerful capability and flexibility in model development as well as the rich data …