Monthly Electric Energy Demand Forecasting Based on Trend Extraction

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

doi:10.1109/tpwrs.2006.883666

Cited by 211 publications

(77 citation statements)

References 27 publications

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“…Long-term forecasts of the peak electricity demand are needed for capacity planning and maintenance scheduling [1]. Mediumterm demand forecasts are required for power system operation and planning [2]. Short-term load forecasts are required for the control and scheduling of power systems.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Taylor

McSharry

2007

IEEE Trans. Power Syst.

495

219

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Taylor

McSharry

2007

IEEE Trans. Power Syst.

495

219

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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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“…Different forecasting models are used in this case such as classical autoregressive integrated moving average (ARIMA) and multiple linear regression [5], as well as computational intelligence methods, e.g. neural networks [6]. Examples of such models can be found in [7], where ARIMA, neural networks and neuro-fuzzy systems are employed to forecast future load demand based on various weather-related parameters and historical load profiles.…”

Section: Introductionmentioning

confidence: 99%

Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

Pełka

Dudek

2017

ITM Web Conf.

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Abstract. Electricity demand forecasting is of important role in power system planning and operation. In this work, fuzzy nearest neighbour regression has been utilised to estimate monthly electricity demands. The forecasting model was based on the pre-processed energy consumption time series, where input and output variables were defined as patterns representing unified fragments of the time series. Relationships between inputs and outputs, which were simplified due to patterns, were modelled using nonparametric regression with weighting function defined as a fuzzy membership of learning points to the neighbourhood of a query point. In an experimental part of the work the model was evaluated using real-world data. The results are encouraging and show high performances of the model and its competitiveness compared to other forecasting models.

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Monthly Electric Energy Demand Forecasting Based on Trend Extraction

Cited by 211 publications

References 27 publications

Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Short-Term Load Forecasting Methods: An Evaluation Based on European Data

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

Prediction of monthly electric energy consumption using pattern-based fuzzy nearest neighbour regression

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