A study on neural networks for short-term load forecasting

Lee, K.Y.; Cha, Ye; Ku, Chao-Chee

doi:10.1109/ann.1991.213492

Cited by 33 publications

(7 citation statements)

References 10 publications

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“…Recently, a large number of studies have tried to apply neural networks to short term electric load forecasting (El-Sharkawi et al, 1991;Srinivasan et al, 1991;Hwang and Moon, 1991;Chen et af., 1991;Lee et al, 1991Lee et al, , 1992Hsu and Yang, 1991;Brace et al, 1991, among others). All these studies required a large amount of data and had large network structures.…”

Section: Relevant Backgroundmentioning

confidence: 98%

Back propagation in time‐series forecasting

Lachtermacher

Fuller

1995

Journal of Forecasting

140

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One of the major constraints on the use of back propagation neural networks as a practical forecasting tool is the number of training patterns needed. We propose a methodology that reduces the data requirements. The general idea is to use the Box‐Jenkins model in an exploratory phase to identify the 'lag components' of the series, to determine a compact network structure with one input unit for each lag, and then apply the validation procedure. This process minimizes the size of the network and consequently the data required to train the network. The results obtained in eight studies show the potential of the new methodology as an alternative to the traditional time‐series models.

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Section: Relevant Backgroundmentioning

confidence: 98%

Back propagation in time‐series forecasting

Lachtermacher

Fuller

1995

Journal of Forecasting

140

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“…The other load models are based on the load behavior, which is represented by a Fourier series or trend curves in terms of time functions. In addition, state variable models and autoregressive-moving average (ARMA) models have also been developed to describe the load behavior [1], [4], [5].…”

Section: A Parametric Load Forecasting Methodsmentioning

confidence: 99%

Short-Term Load Forecasting Using System-Type Neural Network Architecture

Kim

Velas

Lee

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Neural networks have been applied in various new ways to the problem of short-term load forecasting for power systems. Virtually all of these methods are based on using statistical patterns, which are perceived between the yearly load histories of the system to predict the forecasted year's demand. The proposed method also uses a neural network approach, but differs from the others in how those patterns are perceived. Specifically, the proposed approach begins with the premise that the load demand for a given year can be given a structure which can then be related to the structure of the reference year, in such a way that a transformation can be found from the reference year's structure to the forecasting year's structure. The transformation depends upon how parameters, which influenced the load but can not be measured, move from the reference year to the forecasting year.

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“…In the literature, different methods were proposed to provide an accurate model of LF. In [1][2][3][4][5], the models proposed were based on artificial neural networks (ANNs), time functions, and IET Gener. Transm.…”

Section: Literature Reviewmentioning

confidence: 99%

Gaussian process regression‐based load forecasting model

Yadav

Bareth

Kochar

et al. 2023

IET Generation Trans & Dist

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In this paper, Gaussian Process Regression (GPR)‐based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non‐parametric kernel‐based learning method having the ability to provide correct predictions with uncertainty in measurements. The proposed model provides an hourly and monthly load forecast for an Australian city and four Indian cities in the Maharashtra state. Twelve GPR models are trained with historical datasets including hourly load and environmental data. To evaluate the trained model, the actual and predicted load demand curve is plotted and mean average percentage error (MAPE) is calculated corresponding to different kernel functions of the GPR model. To the best of the author's knowledge, the prediction of load demand using GPR for Indian cities of Maharashtra state has been made for the first time. The calculated MAPE in LF is 0.15% for Australia and 0.002%, 0.209%, 0.077%, and 0.140% for Indian cities viz. Nasik, Bhusawal, Kolhapur, and Aurangabad, respectively. The test results illustrate that minimum MAPE in load prediction is obtained using the proposed model that is GPR with ‘Exponential’ kernel functions. Furthermore, the comparative analysis with the existing approaches confirms the dominance of the proposed model.

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A study on neural networks for short-term load forecasting

Cited by 33 publications

References 10 publications

Back propagation in time‐series forecasting

Back propagation in time‐series forecasting

Short-Term Load Forecasting Using System-Type Neural Network Architecture

Gaussian process regression‐based load forecasting model

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