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 …
Systematic review of electricity demand forecast using ANN-based machine learning algorithms
A Román-Portabales, M López-Nores, JJ Pazos-Arias - Sensors, 2021 - mdpi.com
The forecast of electricity demand has been a recurrent research topic for decades, due to its
economical and strategic relevance. Several Machine Learning (ML) techniques have …
economical and strategic relevance. Several Machine Learning (ML) techniques have …
Forecasting of electric load using a hybrid LSTM-neural prophet model
Load forecasting (LF) is an essential factor in power system management. LF helps the utility
maximize the utilization of power-generating plants and schedule them both reliably and …
maximize the utilization of power-generating plants and schedule them both reliably and …
Short-term residential load forecasting using graph convolutional recurrent neural networks
The abundance of energy consumption data collected by smart meters has inspired
researchers to employ deep neural networks to solve the existing problems in the power …
researchers to employ deep neural networks to solve the existing problems in the power …
Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior
X Zhang, X Kong, R Yan, Y Liu, P **a, X Sun, R Zeng… - Energy, 2023 - Elsevier
The access of electric vehicles facilitates in the fluctuation and diversification of building
load, accurate load prediction contributes to investigating the operation and optimization of …
load, accurate load prediction contributes to investigating the operation and optimization of …
Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions
In this paper, we propose a scalable Big Data framework that collects the data from smart
meters and weather sensors, pre-processes and loads it into a NoSQL database that is …
meters and weather sensors, pre-processes and loads it into a NoSQL database that is …
Bifurcations and loss jumps in RNN training
Recurrent neural networks (RNNs) are popular machine learning tools for modeling and
forecasting sequential data and for inferring dynamical systems (DS) from observed time …
forecasting sequential data and for inferring dynamical systems (DS) from observed time …
A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks
Y Wang, L Rui, J Ma - Applied Soft Computing, 2023 - Elsevier
Accurate residential load forecasting (RLF) is of great significance for the decision-making
and operation of modern power system. In literature, deep neural network (DNN) based RLF …
and operation of modern power system. In literature, deep neural network (DNN) based RLF …
Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring
Home energy management systems (HEMS) enable key strategies and methods to improve
residential efficiency and energy utilization. To make informed decisions, HEMS depend on …
residential efficiency and energy utilization. To make informed decisions, HEMS depend on …
GrAb: A Deep Learning-Based Data-Driven Analytics Scheme for Energy Theft Detection
Integrating information and communication technology (ICT) and energy grid infrastructures
introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The …
introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The …