An ensemble approach for short-term load forecasting by extreme learning machine
Applied EnergyPublished 1 March 2016Open access
Li Song, L. Goel, Peng Wang
Citations250
SJR quartileQ1
SJR score2.90
SNIP2.51
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Numerical results show that the proposed method for short-term load forecasting based on wavelet transform, extreme learning machine (ELM), and partial least squares regression can significantly improve forecasting performance.
Abstract
This paper proposes a novel ensemble method for short-term load forecasting based on wavelet transform, extreme learning machine (ELM) and partial least squares regression. In order to improve forecasting performance, a wavelet-based ensemble strategy is introduced into the forecasting model. The individual forecasters are derived from different combinations of mother wavelet and number of decomposition levels. For each sub-component from the wavelet decomposition, a parallel model consisting of 24 ELMs is invoked to predict the hourly load of the next day. The individual forecasts are then combined to form the ensemble forecast using the partial least squares regression method. Numerical results show that the proposed method can significantly improve forecasting performance.
Keywords
Computer ScienceDecision SciencesEngineering
Computers in PhysicsTen Lectures on Wavelets
15,386 Citations1992Ingrid Daubechies, Christopher Heil
This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Analytica Chimica ActaPartial least-squares regression: a tutorial
6,934 Citations1986Paul Geladi, Bruce R. Kowalski
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)Extreme Learning Machine for Regression and Multiclass Classification
5,493 Citations2011Guang-Bin Huang, Hongming Zhou +2 more
ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
IEEE Transactions on Power SystemsNeural networks for short-term load forecasting: a review and evaluation
2,202 Citations2001Henrique S. Hippert, Carlos E. Pedreira +1 more
This review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting, and critically evaluating the ways in which the NNs proposed in these papers were designed and tested.
Chemometrics and Intelligent Laboratory SystemsSIMPLS: An alternative approach to partial least squares regression
1,877 Citations1993Sijmen de Jong
Lecture notes in computer scienceOverview and Recent Advances in Partial Least Squares
1,073 Citations2006Roman Rosipal, Nicole Krämer
Partial Least Squares is a wide class of methods for modeling relations between sets of observed variables by means of latent variables as well as dimension reduction techniques and modeling tools.
IEEE Transactions on Power SystemsThe Time Series Approach to Short Term Load Forecasting
536 Citations1987Martin Hagan, Suzanne M. Behr
It is shown than Box and Jenkins time series models, in particular, are well suited to this application and one of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature.
IEEE Transactions on Power SystemsShort-term hourly load forecasting using time-series modeling with peak load estimation capability
507 Citations2001Nima Amjady
This paper presents a new time series modeling for short term load forecasting, which can model the valuable experiences of the expert operators and can exactly forecast the daily peak load of a power system.
IEEE Transactions on Power SystemsA Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
467 Citations2013Ervin Čeperić, Vladimir Čeperić +1 more
Two important improvements to the SVR based load forecasting method are introduced, i.e., procedure for generation of model inputs and subsequent model input selection using feature selection algorithms and the use of the particle swarm global optimization based technique for the optimization of SVR hyper-parameters reduces the operator interaction.
IEEE Transactions on Power SystemsNeural network load forecasting with weather ensemble predictions
451 Citations2002James W. Taylor, Roberto Buizza
IEEE Transactions on Power SystemsShort-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks
438 Citations2009Ying Chen, Peter B. Luh +6 more
A similar day-based wavelet neural network method to forecast tomorrow's load by selecting similar day load as the input load based on correlation analysis, and using wavelet decomposition and separate neural networks to capture the features of load at low and high frequencies.
IEEE Transactions on Power SystemsApplying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks
409 Citations2009Zia Bashir, M.E. El-Hawary
A particle swarm optimization (PSO) algorithm is employed to adjust the network's weights in the training phase of the ANNs to create a more reliable forecasting model.
Journal of Machine Learning ResearchManaging Diversity in Regression Ensembles
345 Citations2005Gavin Brown, Jeremy Wyatt +1 more
It is demonstrated that these methods control the bias-variance-covariance trade-off systematically, and can be utilised with any estimator capable of minimising a quadratic error function, for example MLPs, or RBF networks.
EnergyShort-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm
323 Citations2008Nima Amjady, Farshid Keynia
A new hybrid forecast method, composed of wavelet transform, neural network and evolutionary algorithm, is proposed forHourly load time series usually consists of both global smooth trends and sharp local variations, i.e. low- and high-frequency components.
IEEE Transactions on Power SystemsFeature Extraction via Multiresolution Analysis for Short-Term Load Forecasting
282 Citations2005A.J. RochaReis, Alexandre P. AlvesdaSilva
Two strategies for embedding the discrete wavelet transform into neural network-based short-term load forecasting are described, which aim is to develop more robust load forecasters.
EnergyElectric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm
275 Citations2011Wei‐Chiang Hong
This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance and yields more accurate forecasting results than ARIMA and TF-e-SVR-SA models.
Energy Conversion and ManagementArtificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher
273 Citations2003Kostas Metaxiotis, Argyris G. Kagiannas +2 more
It is revealed that although still regarded as a novel methodology, AI technologies are shown to have matured to the point of offering real practical benefits in many of their applications.
TechnometricsComparative Models for Electrical Load Forecasting
269 Citations1986Thomas J. Harris, Derek W. Bunn +1 more
IEEE Transactions on Power SystemsElectricity Price Forecasting With Extreme Learning Machine and Bootstrapping
267 Citations2012Xia Chen, Zhao Yang Dong +4 more
A fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome drawbacks of traditional training methods.
Applied EnergyAn annual load forecasting model based on support vector regression with differential evolution algorithm
258 Citations2012Jianjun Wang, Li Li +2 more
The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
IEEE Signal Processing LettersEnsemble Based Extreme Learning Machine
252 Citations2010Nan Liu, Han Wang
An ensemble based ELM (EN-ELM) algorithm is proposed where ensemble learning and cross-validation are embedded into the training phase so as to alleviate the overtraining problem and enhance the predictive stability.
IEEE Transactions on Industrial ElectronicsA Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies
241 Citations2015Carlo Cecati, Janusz Kolbusz +3 more
This paper investigates the effectiveness of some of the newest designed algorithms in machine learning to train typical radial basis function (RBF) networks for 24-h electric load forecasting: support vector regression (SVR), extreme learning machines (ELMs), decay RBF neural networks (DRNNs), improves second order, and error correction, drawing some conclusions useful for practical implementations.
IEEE Transactions on Power SystemsShort term load forecasting of Taiwan power system using a knowledge-based expert system
236 Citations1990Ku-Long Ho, Yuan‐Yih Hsu +5 more
A knowledge-based expert system was developed for the short-term load forecasting of the Taiwan power system and it is found that a mean absolute error of 2.52% for one year is achieved by the expert system approach as compared to an error of 3.86% by the statistical method.
IEEE Transactions on Power SystemsVery Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering
228 Citations2012Che Guan, Peter B. Luh +3 more
The key idea is to use a spike filtering technique to detect spikes in load data and correct them and this method of wavelet neural networks with data pre-filtering is presented.
Electric Power Systems ResearchShort-term load forecasting by wavelet transform and evolutionary extreme learning machine
207 Citations2015Li Song, Peng Wang +1 more
A novel short-term load forecasting (STLF) method based on wavelet transform, extreme learning machine (ELM) and modified artificial bee colony (MABC) algorithm that can improve the learning accuracy with fewer iteration steps is proposed.
IET Generation Transmission & DistributionShort‐term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine
193 Citations2013Rui Zhang, Zhao Yang Dong +3 more
An ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high-quality STLF of Australian National Electricity Market (NEM) and its performance is compared with some state-of-the-art learning algorithms.
Applied EnergyForecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting
193 Citations2012Fahad Javed, Naveed Arshad +3 more
International Journal of Energy ResearchSupport vector machines for short-term electrical load forecasting
166 Citations2002Mohamed Mohandes
The results indicate that support vector machines compare favourably against the auto‐regressive model using the same data for building and testing both models based on the root‐mean‐square errors between the actual and the predicted data.
Applied EnergyLong term electric load forecasting based on particle swarm optimization
161 Citations2009M. R. AlRashidi, K. M. El-Naggar
A particle swarm optimization is employed to minimize the error associated with the estimated model parameters and the problem is formulated as an estimation problem and presented in state space form.
IEEE Computational Intelligence MagazineShort-Term Load Forecasting with Neural Network Ensembles: A Comparative Study [Application Notes]
154 Citations2011Matteo De Felice, Xin Yao
In future electric grids, a reliable forecasting of load demand could help to avoid dispatch problems given by unexpected loads, and give vital information to make decisions on energy generation and purchase, especially market-based dynamic pricing strategies.
Applied EnergyImproving photovoltaics grid integration through short time forecasting and self-consumption
129 Citations2014D. Masa-Bote, Manuel Castillo-Cagigal +5 more
EnergyApplication of echo state networks in short-term electric load forecasting
123 Citations2012Ali Deihimi, Hemen Showkati
The paper presents the application of echo state network (ESN) to short-term load forecasting (STLF) problem in power systems for both 1-h and 24-h ahead predictions while using the least number of inputs: current-hour load, predicted target-hour temperature, and only for 24-H ahead forecasting, day-type index.
IEEE Transactions on Power SystemsIntelligent Hybrid Wavelet Models for Short-Term Load Forecasting
119 Citations2010Ajay Shekhar Pandey, Devender Singh +1 more
A wavelet decomposition based load forecast approach is proposed for 24-h and 168-h ahead short-term load forecasting and shows the superiority of proposed wavelet based approach over non-wavelet methods for the same set of data of the same utility.
Electrical Load Forecasting : Modeling and Model Construction
105 Citations2010S.A. Soliman, Ahmad M. Al-Kandari
IEEE Transactions on Power SystemsShort-Term Hourly Load Forecasting Using Abductive Networks
101 Citations2004R.E. Abdel-Aal
This paper proposes using the alternative technique of abductive networks, which offers the advantages of simplified and more automated model synthesis and analytical input-output models that automatically select influential inputs, provide better insight and explanations, and allow comparison with statistical and empirical models.
IEEE Transactions on Power SystemsShort term load forecasting using a self-supervised adaptive neural network
92 Citations1999Hoon Yoo, Russell L. Pimmel
A self-supervised adaptive neural network is developed to perform short term load forecasts (STLF) for a large power system covering a wide service area with several heavy load centers and finds levels of error that compare favorably with those obtained by other techniques.
Applied EnergyA new hybrid day-ahead peak load forecasting method for Iran’s National Grid
69 Citations2012Majid Moazzami, Amin Khodabakhshian +1 more
A neural network based very short term load forecaster for the interim ISO New England electricity market system
59 Citations2002P. Shamsollahi, Kwok W. Cheung +2 more
The development and implementation of an artificial neural network (ANN) based very short-term load forecasting (VSTLF) model for the interim electricity market of ISO New England (ISO-NE) demonstrates a high forecast accuracy.
Electric Power Systems ResearchImproving electric load forecasts using network committees
40 Citations2004R.E. Abdel-Aal
An abductive network three-member committee was developed on data for three successive years and evaluated on the fourth year, and compared to a monolithic model trained on the same full three-year data, the committee reduces the mean absolute percentage error.