Monthly electric energy demand forecasting with neural networks and Fourier series

Romera, Eva González; Jaramillo-Morán, Miguel A.; Carmona-Fernández, Diego

doi:10.1016/j.enconman.2008.06.004

Cited by 115 publications

(53 citation statements)

References 28 publications

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“…The forecasting results and errors from January 2005 to December 2006 are listed in Table 1. Compared with the traditional monthly electric energy consumption forecasting method proposed by González-Romera et al [21] (hereinafter referred to as Method 2), two primary innovations are notable in Method 1. They are the applications of DWT and GM.…”

Section: Experimental Setup and Forecasting Resultsmentioning

confidence: 99%

“…Zhao and Wei [18] have summarized a number of methods for extracting the series features. González-Romera et al [19][20][21] adopted a moving average algorithm to extract the rising trend from a monthly electric energy demand series. The width of the data window in the moving average is selected by measuring the fitting accuracy and the smoothness of the obtained rising figure.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Monthly Electric Energy Consumption Using Feature Extraction

2011

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Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly electricity consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on electric energy consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.

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Section: Experimental Setup and Forecasting Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

2011

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“…A mean absolute percentage error of about 2% was obtained. The same authors, González-Romera et al, proposed a novel hybrid approach to investigate the periodic behavior of the Spanish monthly electric demand series [49]: this behavior is forecasted with a Fourier series [50] while the trend is predicted using an artificial neural network. Satisfactory results were obtained.…”

Section: Figure 1 Diagram Of the Optienr Project In Red The Forecamentioning

confidence: 99%

Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption

Eynard

Grieu

Polit

2011

Engineering Applications of Artificial Intelligence

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To cite this version:Julien Eynard, Stéphane Grieu, Monique Polit. Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption. Engineering Applications of Artificial Intelligence, Elsevier, 2011, 24 (3) Domitia, 52 Av. Paul Alduy, 66860, Perpignan, Abstract: as part of the OptiEnR research project, the present paper deals with outdoor temperature and thermal power consumption forecasting. This project focuses on optimizing the functioning of a multi-energy district boiler (La Rochelle, west coast of France), adding to the plant a thermal storage unit and implementing a model-based predictive controller. The proposed short-term forecast method is based on the concept of time series and uses both a wavelet-based multi-resolution analysis and multi-layer artificial neural networks. One could speak of "MRA-ANN" methodology. The discrete wavelet transform allows decomposing sequences of past data in subsequences (named coefficients) according to different frequency domains, while preserving their temporal characteristics. From these coefficients, multi-layer Perceptrons are used to estimate future subsequences of 4 hours and 30 minutes. Future values of outdoor temperature and thermal power consumption are then obtained by simply summing up the estimated coefficients. Substituting the prediction task of an original time series of high variability by the estimation of its wavelet coefficients on different levels of lower variability is the main idea of the present work. In addition, the sequences of past data are completed, for each of their components, by both the minute of the day and the day of the year to place the developed model in time. The present paper mainly focuses on the impact on forecast accuracy of various parameters, related with the discrete wavelet transform, such as both the wavelet order and the decomposition level, and the topology of the neural networks used. The number of past sequences to take into account and the chosen time step were also major concerns. The optimal configuration for the tools used leads to very good forecasting results and validates the proposed MRA-ANN methodology.

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“…There are several studies based on statistical or machine learning prediction in the literature, which deal with seasonality. González-Romera et al (2008) investigated the periodic behavior of the Spanish monthly electricity demand series, and the authors proposed a novel hybrid approach based on a Fourier series while the trend is predicted with a neural network. Wang et al (2009) proposed a combined e-SVR model considering seasonal proportions based on development tendencies from historical data, which forecasted the northeast electricity demand of China.…”

Section: Introductionmentioning

confidence: 99%