Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction

Deihimi, Ali; Orang, Omid; Showkati, Hemen

doi:10.1016/j.energy.2013.06.007

Cited by 84 publications

(46 citation statements)

References 49 publications

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“…In recent years, a number of applications of ESN in streamflow forecasting [7][8][9] for hydropower plant and load forecasting [10][11][12] for power system have been revealed in the literature. The results indicate that ESN not only benefits from some feedbacks like other RNNs that enable them to model any complex dynamic behavior, but also gains a sparsely interconnected reservoir of neurons leading to a very fast and simple training procedure, unlike the complicated and time consuming training process of other RNNs without reservoir.…”

Section: Introductionmentioning

confidence: 99%

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

et al. 2015

Energies

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Abstract:As a novel recurrent neural network (RNN), an echo state network (ESN) that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN) is proposed for short-term power production forecasting of small hydropower (SHP) plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN) and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN.

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Section: Introductionmentioning

confidence: 99%

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

et al. 2015

Energies

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“…The low-frequency component is an approximation of the original signal representing its general trend while the highfrequency component provides a detailed representation [13]. Generally, the discrete wavelet transform of a discrete time signal f(k) is defined by [19]:…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

A hybrid short-term load forecasting with a new data preprocessing framework

Ghayekhloo

Menhaj

Ghofrani

2015

Electric Power Systems Research

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“…How to effectively improve the accuracy of electricity demand prediction has become a major challenge to researchers [8]. At present, the methods used for short-term electricity demand prediction mainly include time series [9][10][11], Regression Analysis [12,13], Support Vector Regression [14][15][16], Neural Network [17][18][19][20], Bayes [21], Fuzzy Theory [20,22], and Wavelet Echo State Network [23]. Each kind of method has its own applicable scenario, and no model can achieve desired satisfying result alone.…”

Section: Related Workmentioning

confidence: 99%

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Zheng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototypebased clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users' historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user's electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

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Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction

Cited by 84 publications

References 49 publications

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

A hybrid short-term load forecasting with a new data preprocessing framework

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

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