Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis
Authors
Keywords
-
Journal
Processes
Volume 8, Issue 4, Pages 484
Publisher
MDPI AG
Online
2020-04-22
DOI
10.3390/pr8040484
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Predicting residential energy consumption using CNN-LSTM neural networks
- (2019) Tae-Young Kim et al. ENERGY
- A Widespread Review of Smart Grids Towards Smart Cities
- (2019) Mina Farmanbar et al. Energies
- Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †
- (2018) Salah Bouktif et al. Energies
- Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data
- (2018) Magnus Dahl et al. Energies
- Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
- (2018) Weicong Kong et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
- (2018) Yi Wang et al. IEEE Transactions on Smart Grid
- Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees
- (2018) María Ruiz-Abellón et al. Energies
- Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network
- (2017) Weicong Kong et al. IEEE Transactions on Smart Grid
- A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building
- (2016) Hamid Khosravani et al. Energies
- Deep Neural Network Based Demand Side Short Term Load Forecasting
- (2016) Seunghyoung Ryu et al. Energies
- Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach
- (2015) Xiaoshu Lü et al. APPLIED ENERGY
- Day-ahead load forecast using random forest and expert input selection
- (2015) A. Lahouar et al. ENERGY CONVERSION AND MANAGEMENT
- Regression analysis for prediction of residential energy consumption
- (2015) Nelson Fumo et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities
- (2015) Franklin L. Quilumba et al. IEEE Transactions on Smart Grid
- A review on applications of ANN and SVM for building electrical energy consumption forecasting
- (2014) A.S. Ahmad et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- A review on the prediction of building energy consumption
- (2012) Hai-xiang Zhao et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- An Empirical Comparison of Machine Learning Models for Time Series Forecasting
- (2010) Nesreen K. Ahmed et al. Econometric Reviews
- An artificial neural network (p,d,q) model for timeseries forecasting
- (2009) Mehdi Khashei et al. EXPERT SYSTEMS WITH APPLICATIONS
- Time Series Analysis Forecasting and Control
- (2009) G. Janacek JOURNAL OF TIME SERIES ANALYSIS
- Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
- (2008) S.Sp. Pappas et al. ENERGY
- A review on buildings energy consumption information
- (2007) Luis Pérez-Lombard et al. ENERGY AND BUILDINGS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started