Combining a neural network and a rule-based expert system for short-term load forecasting

Chiu, Chih‐Chou; Cook, Deborah F.; Kao, Jen-Lung; Chou, Yu-Chao

doi:10.1016/s0360-8352(97)00009-0

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…In general, RBF network is a three-layer network. The RBF network structure that is used in this paper [16][17][18][19] is shown as Fig. (1).…”

Section: The Rbf Neural Network Forecasting Modelmentioning

confidence: 99%

“…In recent years, considerable attention has been given to ANNs, unlike energy analysis based on analytical models and industrial methods. Much reported study on ANN applications in the field of thermal load prediction proves that ANN is successful in prediction areas [11][12][13][14][15][16][17][18]. The ANN is good for some tasks while lacking in some others [11].…”

Section: Introductionmentioning

confidence: 99%

“…In general, ANN has the advantages of self adaptability and the the ability of learning, but its optimal network structure is difficult to find and it needs a lot of train sample. The accuracy of forecasting can still be improved, and ANN is usually applied for short-term prediction [18].…”

Section: Introductionmentioning

confidence: 99%

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An Air-conditioning Load Forecasting Based on Dynamical Combined Residual Error Correction

Zhang¹,

Ren²,

Li³

2015

TOAUTOCJ

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Accurate airconditioning load forecasting is the precondition for the optimal control and energy saving operation of central airconditioning system. However, the single forecasting method, such as autoregressive integrated moving average (ARIMA), grey model (GM), multiple linear regression (MLR) and artificial neural network (ANN), has not enough accuracy. In order to improve the accuracy of airconditioning load forecasting, the combination forecast develops. But so far there are no literatures that explain how to choose the single forecasting methods to build the combination forecast that can further improve the forecasting accuracy. To further improve the forecasting accuracy, a forecasting method with dynamical combined residual error correction is proposed. The residual error correction model and its combination ways are analyzed, and the very high accuracy with mathematical proof is realized in this paper. A case study indicates that the dynamical combination ways proposed in this paper can further improve the accuracy of combination forecasting and satisfy the accuracy requirement of airconditioning load forecasting.

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“…In general, RBF network is a three-layer network. The RBF network structure that is used in this paper [16][17][18][19] is shown as Fig. (1).…”

Section: The Rbf Neural Network Forecasting Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Air-conditioning Load Forecasting Based on Dynamical Combined Residual Error Correction

Zhang¹,

Ren²,

Li³

2015

TOAUTOCJ

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Modeling and Forecasting Electricity Loads and Prices

Weron¹

2006

648

651

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In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components -a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed seasonal volatility technique. In both models the stochastic component is described by an ARMA time series. Models are tested on a time series of system-wide loads from the California power market and compared with the official forecast of the California System Operator (CAISO).

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2006

Modeling and Forecasting Electricity Loads and Prices

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Combining a neural network and a rule-based expert system for short-term load forecasting

Cited by 10 publications

References 14 publications

An Air-conditioning Load Forecasting Based on Dynamical Combined Residual Error Correction

An Air-conditioning Load Forecasting Based on Dynamical Combined Residual Error Correction

Modeling and Forecasting Electricity Loads and Prices

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