Short-term load forecasting using lifting scheme and ARIMA models

Lee, Cheng-Ming; Ko, Chia-Nan

doi:10.1016/j.eswa.2010.11.033

Cited by 340 publications

(130 citation statements)

References 28 publications

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“…Cottet & Smith (Cottet and Smith, 2003) have used for this purpose the procedures by Bayes; Blum & Riedmiller (Blum and Riedmiller, 2013) predict energy demand using Gaussian processes, and Dongxiao et al (Dongxiao et al, 2010) use a support vector machine and ant colony optimization. However, the most popular models used to forecast electricity demand are ARMA / ARIMA / SARIMA models and exponential smoothing, which have been used, among others, in the works (Pappas et al, 2008;Taylor, 2003;Chen, et al, 1995;Lee and Ko, 2011;De Andrade &and da Silva, 2009;Bratu, 2012;Kasperowicz, 2014a,b) and they have been selected in this article to calculate forecasts for the Polish energy system.…”

Section: A Review Of Researchmentioning

confidence: 99%

A Mid-Term Forecast of Maximum Demand for Electricity in Poland

Brożyna¹,

Mentel²,

Szetela³

2016

MJE

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Forecasting of demand for electricity plays an important role in planning of seasonal operations and expansion of power facilities. Power plants must ensure the continuity of supply of energy, without producing the excess due to problems associated with its storage, and at the same time they must be prepared for increased demand e.g. during the periods of cold weather. These factors make that energy companies need accurate forecasts, which in the best way will help plan all the processes associated with this activity. In the article, based on data from the years 2002-2015, the medium-term forecast of electricity demand in Poland until the end of 2017 has been presented. Forecasts have been determined using the exponential smoothing Winters and SARIMA models, and their results have been compared with each other and the months, when a rise and a fall for energy demand is predicted, have been quoted.

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Section: A Review Of Researchmentioning

confidence: 99%

A Mid-Term Forecast of Maximum Demand for Electricity in Poland

Brożyna¹,

Mentel²,

Szetela³

2016

MJE

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“…Classical approaches of time-series forecasting based on linear models include the Autoregressive Moving Average (ARMA) [6], Autoregressive Integrated Moving Average (ARIMA) [7], Generalized Autoregressive Conditional Heteroskedastic (GARCH) [8], and the variants of these models. Nonlinear techniques feature the Artificial Neural Network (ANN) and its variants (e.g.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Load Forecasting of Active and Reactive Powers in Small Scales using LASSO-Integrated Nonlinear Autoregressive Neural Networks

Zhang¹

2016

Preprint

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Abstract-Short-term load forecasting (STLF) is important for power system planning and optimization, especially in the dynamic environment of smart grid. Traditional load forecasting is implemented at substation levels to predict the upcoming active power and optimal system settings. In more advanced smart grid applications, e.g. the Volt-VAR Control, small-scale load forecasting opens up new opportunities in coordinating distributed resources such as distributed generation (DG) with utilities' efficiency missions. This paper proposes a STLF approach for small residential blocks with 10-12 households. The Nonlinear Autoregressive Neural Network (NAR-NN) is employed to predict hour-ahead active (P) and reactive (Q) powers with a moving window of training data. The regressor shrinkage technique, LASSO, is used to improve the selection of the regressors in the NAR-NN model by removing insignificant input features. The results show the forecasting performance could be enhanced by 20% comparing to feed-forward Artificial Neural Networks (ANNs). The improvement in forecasting both P & Q could accommodate new smart grid applications in small scales.Index Terms-Short-term load forecasting, nonlinear autoregressive neural network, regression shrinkage, power system planning, smart grid.

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“…An excellent review of neural networks for short-term load forecasting has been presented in [11]. However, no single model has performed well in short term load forecasting [12]. This has led to the development of hybrid models that try to deduct the best features of different models and integrate them to achieve good forecasting results [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Simple Hybrid Model for Short-Term Load Forecasting

Annamareddi

Sudheer

Dora

2013

Journal of Engineering

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The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

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Short-term load forecasting using lifting scheme and ARIMA models

Cited by 340 publications

References 28 publications

A Mid-Term Forecast of Maximum Demand for Electricity in Poland

A Mid-Term Forecast of Maximum Demand for Electricity in Poland

Short-Term Load Forecasting of Active and Reactive Powers in Small Scales using LASSO-Integrated Nonlinear Autoregressive Neural Networks

A Simple Hybrid Model for Short-Term Load Forecasting

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