相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Weicong Kong et al.
IEEE TRANSACTIONS ON SMART GRID (2019)
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
Yi Wang et al.
IEEE TRANSACTIONS ON SMART GRID (2019)
Predicting residential energy consumption using CNN-LSTM neural networks
Tae-Young Kim et al.
ENERGY (2019)
A Widespread Review of Smart Grids Towards Smart Cities
Mina Farmanbar et al.
ENERGIES (2019)
Evaluating Feature Selection Methods for Short-term Load Forecasting
Aida Mehdipour Pirbazari et al.
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) (2019)
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
Salah Bouktif et al.
ENERGIES (2018)
Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data
Magnus Dahl et al.
ENERGIES (2018)
Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
Weicong Kong et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2018)
Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees
Maria del Carmen Ruiz-Abellon et al.
ENERGIES (2018)
Deep Neural Network Based Demand Side Short Term Load Forecasting
Seunghyoung Ryu et al.
ENERGIES (2017)
A data-driven multi-model methodology with deep feature selection for short-term wind forecasting
Cong Feng et al.
APPLIED ENERGY (2017)
A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building
Hamid R. Khosravani et al.
ENERGIES (2016)
Modeling and forecasting energy consumption for heterogeneous buildings using a physical-statistical approach
Xiaoshu Lu et al.
APPLIED ENERGY (2015)
Day-ahead load forecast using random forest and expert input selection
A. Lahouar et al.
ENERGY CONVERSION AND MANAGEMENT (2015)
Regression analysis for prediction of residential energy consumption
Nelson Fumo et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)
Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities
Franklin L. Quilumba et al.
IEEE TRANSACTIONS ON SMART GRID (2015)
A Review of Short Term Load Forecasting using Artificial Neural Network Models
Arjun Baliyan et al.
INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND CONVERGENCE (ICCC 2015) (2015)
A review on applications of ANN and SVM for building electrical energy consumption forecasting
A. S. Ahmad et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2014)
A review on the prediction of building energy consumption
Hai-xiang Zhao et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2012)
An Empirical Comparison of Machine Learning Models for Time Series Forecasting
Nesreen K. Ahmed et al.
ECONOMETRIC REVIEWS (2010)
An artificial neural network (p, d, q) model for timeseries forecasting
Mehdi Khashei et al.
EXPERT SYSTEMS WITH APPLICATIONS (2010)
Time Series Analysis Forecasting and Control
G. Janacek
JOURNAL OF TIME SERIES ANALYSIS (2009)
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
S. Sp. Pappas et al.
ENERGY (2008)
A review on buildings energy consumption information
Luis Perez-Lombard et al.
ENERGY AND BUILDINGS (2008)
Another look at measures of forecast accuracy
Rob J. Hyndman et al.
INTERNATIONAL JOURNAL OF FORECASTING (2006)
Using support vector machines for time series prediction
U Thiessen et al.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS (2003)
Stochastic gradient boosting
JH Friedman
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)
分享论文
