Journal ArticleDOI
Short term electricity price forecast based on environmentally adapted generalized neuron
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TLDR
In this paper, a generalized neuron model is used for forecasting the short term electricity price of Australian electricity market, the preprocessing of the input parameters is accomplished using wavelet transform for better representation of the low and high frequency components, the free parameters of the generalized neurons model are tuned using environment adaptation method algorithm for increasing the generalization ability and efficacy of the model.About:
This article is published in Energy.The article was published on 2017-04-15. It has received 73 citations till now. The article focuses on the topics: Electricity market & Order (exchange).read more
Citations
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Journal ArticleDOI
Effective long short-term memory with differential evolution algorithm for electricity price prediction
TL;DR: Results indicate that the proposed DE–LSTM model outperforms existing forecasting models in terms of forecasting accuracies and is designed to identify suitable hyperparameters for LSTM.
Journal ArticleDOI
Machine learning in energy economics and finance: A review
TL;DR: A review of the burgeoning literature dedicated to Energy Economics/Finance applications of ML suggests that Support Vector Machine, Artificial Neural Network, and Genetic Algorithms are among the most popular techniques used in energy economics papers.
Journal ArticleDOI
Forecasting methods in energy planning models
TL;DR: A systematic and critical review of forecasting methods used in 483 EPMs, finding that computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data.
Journal ArticleDOI
Electricity Price Prediction Based on Hybrid Model of Adam optimized LSTM Neural Network and Wavelet Transform
TL;DR: A new hybrid model based on wavelet transform and Adam optimized LSTM neural network, denoted as WT-Adam-LSTM, is proposed and the results show that the proposed model can significantly improve the prediction accuracy.
Journal ArticleDOI
Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem
Priyanka Singh,Pragya Dwivedi +1 more
TL;DR: A novel evolutionary algorithm based on follow the leader concept is developed and its performance is validated by COmparing Continuous Optimizers experimental framework on the set of 24 Black-Box Optimization Benchmarking functions and it outperformed all state-of-art algorithms in 20-D and ranked second in other dimensions.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Journal ArticleDOI
Forecasting with artificial neural networks: the state of the art
TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.
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