Research on the Forecast Model of Electricity Power Industry Loan Based on GA-BP Neural Network

Liu, Ke; Wenyan, Guo; Xiaoliu, Shen; Tan, Zhongfu

doi:10.1016/j.egypro.2011.12.1188

Cited by 41 publications

(10 citation statements)

References 1 publication

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“…The BP neural network model is applied to a wide field of forecasting. As Ke et al [17] used the genetic algorithm-BP neural network to forecast the electricity power industry loan, Li et al [18] adopted the BP neural network to the prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction. In addition, the BP neural network can also be used for evaluation and classification: Li and Chen [19] utilized the BP neural network algorithm to study the sustainable development evaluation of highway construction project.…”

Section: Introductionmentioning

confidence: 99%

A New Strategy for Short-Term Load Forecasting

Yang

Chen

et al. 2013

Abstract and Applied Analysis

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Electricity is a special energy which is hard to store, so the electricity demand forecasting remains an important problem. Accurate short-term load forecasting (STLF) plays a vital role in power systems because it is the essential part of power system planning and operation, and it is also fundamental in many applications. Considering that an individual forecasting model usually cannot work very well for STLF, a hybrid model based on the seasonal ARIMA model and BP neural network is presented in this paper to improve the forecasting accuracy. Firstly the seasonal ARIMA model is adopted to forecast the electric load demand day ahead; then, by using the residual load demand series obtained in this forecasting process as the original series, the follow-up residual series is forecasted by BP neural network; finally, by summing up the forecasted residual series and the forecasted load demand series got by seasonal ARIMA model, the final load demand forecasting series is obtained. Case studies show that the new strategy is quite useful to improve the accuracy of STLF.

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Section: Introductionmentioning

confidence: 99%

A New Strategy for Short-Term Load Forecasting

Yang

Chen

et al. 2013

Abstract and Applied Analysis

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“…BPNN is a kind of typical forward network [18]. The training function is used reversely to change weights and thresholds through the positive transfer method of network structure.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Liu

Wang

Sun

et al. 2015

Mathematical Problems in Engineering

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The method that the real-coded quantum-inspired genetic algorithm (RQGA) used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA) is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

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“…The connection weights are adjusted iteratively by a process of minimizing the forecast errors. BPNN are widely used in economic areas [14], engineering [15], and weather forecasting areas [16]. They have also been introduced into forecasting the incident cases of Hepatitis A [17].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

et al. 2013

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Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

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Research on the Forecast Model of Electricity Power Industry Loan Based on GA-BP Neural Network

Cited by 41 publications

References 1 publication

A New Strategy for Short-Term Load Forecasting

A New Strategy for Short-Term Load Forecasting

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

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