A Hybrid Forecasting Model Based on EMD-GASVM-RBFNN for Power Grid Investment Demand

Li, Jinchao; Zhu, Shaowen; Wu, Qiqi; Zhang, Pengfei

doi:10.1155/2018/7416037

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…Despite the fact, the prediction methods with decomposition algorithm can effectively identify and extract the internal features and laws of nonlinear non-stationary time series. The combination the two method can improve the prediction accuracy of nonlinear non-stationary timeseries [5].…”

Section: Discussionmentioning

confidence: 99%

“…It is proposed by Vapnik. Vapnik developed this method with the basis of dimension theory of VC (Vapnik-Chervonenkis) and also the principle of SRM (Structural Risk Minimization) [5]. To obtain the best promotion ability, SVM looks for the best arrangement between the complexity of the model and its learning ability.…”

Section: Svm Modelmentioning

confidence: 99%

“…For that reason, the accuracy of the DWT decomposition is quite lower than the EMD. However, the EMD model can be coupled with vector machine of discrete wavelet support to overcome these limitations [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

Mitec¹,

Rafidah²,

Shabri³

et al. 2020

IJITEE

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This paper mainly discussed on the forecast of Thailand tourist visiting Malaysia. This paper proposed a three-stage technique in which the empirical mode decomposition (EMD) is combined with wavelet methods and support vector machine model. We used the proposed technique, EMD_WSVM to forecast two ASEAN country tourism timeseries. Detail experiments are conducted for the proposed method, in which there is a comparison between the EMD_WSVM, WSVM and SVM methods. The proposed EMD_WSVM model is determined to be dominant to the other methods in predicting the number of tourist arrivals

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Section: Discussionmentioning

confidence: 99%

Section: Svm Modelmentioning

confidence: 99%

See 1 more Smart Citation

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

Mitec¹,

Rafidah²,

Shabri³

et al. 2020

IJITEE

View full text Add to dashboard Cite

show abstract

“…The characteristic sequence of power grid investment demand was complex, nonlinear, and non-stationary. Firstly, the EMD method was used to deal with the original data in [52]. Then, the power grid investment data sequence was decomposed into two different sub-sequences.…”

Section: Figure 5 Power Grid Investment Demand Forecasting Methodsmentioning

confidence: 99%

A Survey on Investment Demand Assessment Models for Power Grid Infrastructure

Chen

et al. 2021

IEEE Access

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The investment demand forecast of the power grid is the further deepening of grid supply and demand management. Under the current reform of the power system in China, we are facing new challenges in analyzing and predicting the investment demand of the power grid. Firstly, the influencing factors of power grid investment demand from both internal and external aspects are analyzed in this paper. Then the selection principles of power grid investment demand factors by domestic and foreign scholars are summarized in this paper. Based on the above content, the forecast indicators and selection methods of domestic and foreign scholars on power grid investment demand are summarized in this paper. The common influencing factor mining algorithms are the grey correlation analysis, the core principal component analysis, and their combination methods. Moreover, the research status of domestic and foreign scholars in power grid investment demand, data mining technology, and forecasting technology is investigated. The current situation of power grid investment demand in China and the common forecasting methods are also elaborated. Finally, the key issues that need to be solved in future research on the investment demand of power grid infrastructure are summarized in this paper. This review will help power utilities find problems in power grid infrastructure investment demand and guide the future investment demand calculations of power utilities. INDEX TERMS Grid infrastructure, power grid investment, demand forecasting, indicator system, power utilities Power Demand Forecasting Methods Classic Forecasting Methods Traditional Forecasting Methods Modern Forecasting Methods Unit Consumption Method Power Elasticity Coefficient Method Per Capita Electricity Consumption Conversion Algorithm

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“…3) Support Vector Machine SVM forecasting method was proposed by Vapnik [46]. It is based on statistical learning theory.…”

Section: Time Series Datamentioning

confidence: 99%

ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load

et al. 2020

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The significance of electricity cannot be overlooked as all fields of life like material production, health care, educational sector, etc., depend upon it to render consistent and high-quality services, increase productivity and business continuity. To this end, energy operators have experienced a continuous increasing trend in the electricity demand for the past few decades. This may cause many issues like load shedding, increased electricity bills, imbalance between supply and demand, etc. Therefore, forecasting of electricity demand using efficient techniques is crucial for the energy operators to decide about optimal unit commitment and to make electricity dispatch plans. It also helps to avoid wastage as well as the shortage of energy. In this study, a novel forecasting model, known as ELS-net is proposed, which is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short-Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear intrinsic mode functions (IMFs), EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. Using separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. Moreover, SVM requires low computational time as compared to EBiLSTM for linear IMFs. Simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). For performance evaluation, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used as performance metrics. From the simulation results, it is obvious that the proposed ELS-net model outperforms the start-of-the-art techniques, such as EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time.

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A Hybrid Forecasting Model Based on EMD-GASVM-RBFNN for Power Grid Investment Demand

Cited by 9 publications

References 38 publications

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

A Survey on Investment Demand Assessment Models for Power Grid Infrastructure

ELS-Net: A New Approach to Forecast Decomposed Intrinsic Mode Functions of Electricity Load

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