Forecasting of the electric energy demand trend and monthly fluctuation with neural networks

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

doi:10.1016/j.cie.2006.12.010

Cited by 54 publications

(26 citation statements)

References 12 publications

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“…In 2007, González-Romera et al [48] used artificial neural networks to extract the trend component from monthly electric demand data and then performed separate predictions of both tendency and fluctuation, which were summed up to obtain the series forecasting. A mean absolute percentage error of about 2% was 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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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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“…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%

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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“…PAO [4] proposed an ANN model for forecasting Taiwan's electricity consumption. GONZÁLEZ-ROMERA et al [5] proposed the use of neural networks for extraction of both growing and fluctuating trends in monthly electricity load demand. HAMZACEBI [6] explored forecast of Turkey's net electricity energy consumption on sectoral bases until 2020 by applying ANNs.…”

Section: Introductionmentioning

confidence: 99%

Developing energy forecasting model using hybrid artificial intelligence method

Mollaiy-Berneti

2015

J. Cent. South Univ.

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An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation (BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand (gross domestic product (GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand (population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.

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Forecasting of the electric energy demand trend and monthly fluctuation with neural networks

Cited by 54 publications

References 12 publications

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

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

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

Developing energy forecasting model using hybrid artificial intelligence method

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