A Design of Short-Term Load Forecasting Structure Based on ARIMA Using Load Pattern Classification

Im, Kyoung-Mi; Lim, Jae-Hyun

doi:10.1007/978-3-642-22309-9_36

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…The most straightforward approach that was investigated used linear regressions models (Conejo et al, 2002). Researchers were testing ARMAX-based models (Wang et al, 2009), ARIMA models in standard (Espinola et al, 2003) and modified versions (Im and Lim, 2011) and originated in economics GARCH models (Hua et al, 2005). Especially, ARIMA models met some practical interest, and these models can be met in commercial applications.…”

Section: Electricity Prices Prediction Taskmentioning

confidence: 99%

Alternative approaches to the prediction of electricity prices

Domański

Gintrowski

2017

IJESM

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Purpose This paper aims to present the results of the comparison between different approaches to the prediction of electricity prices. It is well-known that the properties of the data generation process may prefer some modeling methods over the others. The data having an origin in social or market processes are characterized by unexpectedly wide realization space resulting in the existence of the long tails in the probabilistic density function. These data may not be easy in time series prediction using standard approaches based on the normal distribution assumptions. The electricity prices on the deregulated market fall into this category. Design/methodology/approach The paper presents alternative approaches, i.e. memory-based prediction and fractal approach compared with established nonlinear method of neural networks. The appropriate interpretation of results is supported with the statistical data analysis and data conditioning. These algorithms have been applied to the problem of the energy price prediction on the deregulated electricity market with data from Polish and Austrian energy stock exchanges. Findings The first outcome of the analysis is that there are several situations in the task of time series prediction, when standard modeling approach based on the assumption that each change is independent of the last following random Gaussian bell pattern may not be a true. In this paper, such a case was considered: price data from energy markets. Electricity prices data are biased by the human nature. It is shown that more relevant for data properties was Cauchy probabilistic distribution. Results have shown that alternative approaches may be used and prediction for both data memory-based approach resulted in the best performance. Research limitations/implications “Personalization” of the model is crucial aspect in the whole methodology. All available knowledge should be used on the forecasted phenomenon and incorporate it into the model. In case of the memory-based modeling, it is a specific design of the history searching routine that uses the understanding of the process features. Importance should shift toward methodology structure design and algorithm customization and then to parameter estimation. Such modeling approach may be more descriptive for the user enabling understanding of the process and further iterative improvement in a continuous striving for perfection. Practical implications Memory-based modeling can be practically applied. These models have large potential that is worth to be exploited. One disadvantage of this modeling approach is large calculation effort connected with a need of constant evaluation of large data sets. It was shown that a graphics processing unit (GPU) approach through parallel calculation on the graphical cards can improve it dramatically. Social implications The modeling of the electricity prices has big impact of the daily operation of the electricity traders and distributors. From one side, appropriate modeling can improve performance mitigating risks associated with the process. Thus, the end users should receive higher quality of services ultimately with lower prices and minimized risk of the energy loss incidents. Originality/value The use of the alternative approaches, such as memory-based reasoning or fractals, is very rare in the field of the electricity price forecasting. Thus, it gives a new impact for further research enabling development of better solutions incorporating all available process knowledge and customized hybrid algorithms.

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Section: Electricity Prices Prediction Taskmentioning

confidence: 99%

Alternative approaches to the prediction of electricity prices

Domański

Gintrowski

2017

IJESM

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“…e statistical prediction methods are usually based on mathematical models [11,12]. e establishment of prediction models often includes the regression analysis method [13], the gray model method [14,15], support vector machine (SVM) [16,17], autoregressive integrated moving average (ARIMA) [18], and artificial neural network (ANN) [19]. ese methods often find it difficult to obtain accurate predictions when dealing with complex nonlinear data.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Wang

Bai

et al. 2020

Complexity

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The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.

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“…In [2], an autoregressive moving average (ARMA) model was given for modeling the electricity demand loads. In [3], the autoregressive integrated moving average model (ARIMA) model was designed for forecasting the short-term electricity load. In [4], the ARMA model for short-term load forecasting was identified considering the non-Gaussian process.…”

Section: Introductionmentioning

confidence: 99%

Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

Xie

et al. 2018

Information

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Short-term electrical load forecasting is of great significance to the safe operation, efficient management, and reasonable scheduling of the power grid. However, the electrical load can be affected by different kinds of external disturbances, thus, there exist high levels of uncertainties in the electrical load time series data. As a result, it is a challenging task to obtain accurate forecasting of the short-term electrical load. In order to further improve the forecasting accuracy, this study combines the data-driven long-short-term memory network (LSTM) and extreme learning machine (ELM) to present a hybrid model-based forecasting method for the prediction of short-term electrical loads. In this hybrid model, the LSTM is adopted to extract the deep features of the electrical load while the ELM is used to model the shallow patterns. In order to generate the final forecasting result, the predicted results of the LSTM and ELM are ensembled by the linear regression method. Finally, the proposed method is applied to two real-world electrical load forecasting problems, and detailed experiments are conducted. In order to verify the superiority and advantages of the proposed hybrid model, it is compared with the LSTM model, the ELM model, and the support vector regression (SVR). Experimental and comparison results demonstrate that the proposed hybrid model can give satisfactory performance and can achieve much better performance than the comparative methods in this short-term electrical load forecasting application.

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A Design of Short-Term Load Forecasting Structure Based on ARIMA Using Load Pattern Classification

Cited by 3 publications

References 2 publications

Alternative approaches to the prediction of electricity prices

Alternative approaches to the prediction of electricity prices

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting

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