A hybrid intelligent algorithm based short-term load forecasting approach

Hooshmand, Rahmat‐Allah; Amooshahi, Habib; Parastegari, Moein

doi:10.1016/j.ijepes.2012.09.002

Cited by 125 publications

(56 citation statements)

References 18 publications

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“…(1) The temperature: Temperature is one of these effective features. In previous studies [37,38], temperature was considered as an essential input feature and the The original load data are decomposed to eliminate the current precipitation value for further modeling by using WT. The original short-term load data S and their approximation A1, as well as the detail component D1 decomposed by one-level DWT are shown in Figure 5.…”

Section: Selection Of Inputmentioning

confidence: 99%

“…(1) The temperature: Temperature is one of these effective features. In previous studies [37,38], temperature was considered as an essential input feature and the From Figure 5, it can be clearly seen that A1, which presents the major fluctuation of the original short-term load data, shows high similarity to S; meanwhile, the other minor irregularity neglected by A1 appears in D1. Therefore, A1 is taken as the input data to model for efficiency.…”

Section: Selection Of Inputmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Liang

Niu

et al. 2016

Energies

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Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT) and least squares support vector machine (LSSVM), which is optimized by an improved cuckoo search (CS). To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day's load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

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Section: Selection Of Inputmentioning

confidence: 99%

Section: Selection Of Inputmentioning

confidence: 99%

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Liang

Niu

et al. 2016

Energies

View full text Add to dashboard Cite

show abstract

“…The performance of the DDH model is stable and good when forecasting the electrical load data in one week and different seasons. In addition, we also compare the forecasting performance of the proposed DDH model in this paper to the models in the literature, including [1,4,44,45]. As shown in Table 6, the model in this paper improves the forecasting accuracy by 0.089% compared to the HS-ARTMAP network.…”

Section: Further Experimentsmentioning

confidence: 89%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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“…Examples applied to ST-TSF include: Artificial Neural Networks (ANN) [40,43,44,45,47], evolutionary computation [53], Support Vector Machines (SVM) [49], fuzzy techniques [51], or their combinations [42,46,50,52]. …”

Section: Introductionmentioning

confidence: 99%

Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Rivero

Pucheta

Laboret

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

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This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.

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A hybrid intelligent algorithm based short-term load forecasting approach

Cited by 125 publications

References 18 publications

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

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