Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting

Yang, Yi; Chen, Yanhua; Wang, Yachen; Li, Caihong; Li, Lian

doi:10.1016/j.asoc.2016.07.053

Cited by 134 publications

(47 citation statements)

References 49 publications

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“…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: 81%

“…As is known to all, electricity, as one of the most important energy resources, is difficult to store. A great variety of instability factors can affect the electric system, such as emergencies, holidays, population changes, the weather and more [1]. Therefore, there is a high demand for the generation, transmission and sales of electricity, because excess supply can result in wasted energy resources and in case of excess demand the need for electricity cannot be satisfied.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Further Experimentsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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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show abstract

“…First, when the Choquet fuzzy integral with respect to a λ-fuzzy measure is incorporated into the FLN to consider the interaction among features in the enhanced pattern, the testing results obtained by the additive FLNGM(1,1) can be further improved by the proposed N-FLNGM(1,1). Second, MLPGM(1,1), GPGM(1,1), and Markov-chain sign estimation contributed to estimate residual sign s k in Equation (13) to improve the prediction accuracy of the original GM(1,1) model. The common characteristic of these three models is that N-FLNGM(1,1) outperformed MLPGM(1,1), GPGM(1,1), and Markov-chain sign estimation compared with data used for model fitting and ex-post testing.…”

Section: Discussionmentioning

confidence: 99%

“…Many forecasting methods, including artificial intelligence techniques, multivariate regression, and time series analysis, have frequently been applied to energy demand forecasting [5][6][7][8][9][10][11][12][13]. A large number of samples are required for multivariate regression and time series analysis like the autoregressive integrated moving average (ARIMA) [14].…”

Section: Introductionmentioning

confidence: 99%

Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

2017

Sustainability

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Abstract:Energy demand prediction plays an important role in sustainable development. The GM(1,1) model has drawn our attention to energy demand forecasting because it only needs a few data points to construct a time series model without statistical assumptions. Residual modification is often considered as well to improve the accuracy of predictions. Several residual modification models have been proposed, but they focused on residual sign estimation, whereas the FLNGM(1,1) model using functional-link net (FLN) can estimate the sign as well as the modification range for each predicted residual. However, in the original FLN, an activation function with an inner product assumes that criteria are independent of each other, so additivity might influence the forecasting performance of FLNGM(1,1). Therefore, in this study, we employ the FLN with a fuzzy integral instead of an inner product to propose a nonadditive FLNGM(1,1). Experimental results based on real energy demand cases demonstrate that the proposed grey prediction model performs well compared with other grey residual modification models that use sign estimation and the additive FLNGM(1,1).

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“…Their numerical results showed that the proposed model outperforms all traditional models, including the ESM, ARIMA, BPNN, equal weight hybrid (EWH) model, and random walk (RWM) model. Forecasting stock returns, Rather et al (2015) proposed a novel hybrid model that merges predictions by three individual models: ES, recurrent neural network (RNN), and ARIMA; the optimum weights of each model are identified using GA. Yang et al (2016) presented a combined forecasting model using BPNN, adaptive network-based fuzzy inference system (ANFIS), and SARIMA models, and thus, used a differential evolution metaheuristic algorithm to optimize the weights of a hybrid model. Their experimental case study showed that their proposed method performed better than the three individual methods and had higher accuracy.…”

mentioning

confidence: 99%

Performance evaluation of series and parallel strategies for financial time series forecasting

Khashei

Hajirahimi

2017

Financ Innov

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Background: Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. Given its direct impact on related decisions, various attempts have been made to achieve more accurate and reliable forecasting results, of which the combining of individual models remains a widely applied approach. In general, individual models are combined under two main strategies: series and parallel. While it has been proven that these strategies can improve overall forecasting accuracy, the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model. Methods: Therefore, this study's key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one. Results: Accordingly, the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price. To do so, autoregressive integrated moving average (ARIMA) and multilayer perceptrons (MLPs) are used to construct two series hybrid models, ARIMA-MLP and MLP-ARIMA, and three parallel hybrid models, simple average, linear regression, and genetic algorithm models. Conclusion: The empirical forecasting results for two benchmark datasets, that is, the closing of the Shenzhen Integrated Index (SZII) and that of Standard and Poor's 500 (S&P 500), indicate that although all hybrid models perform better than at least one of their individual components, the series combination strategy produces more accurate hybrid models for financial time series forecasting.

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Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting

Cited by 134 publications

References 49 publications

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

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

Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting

Performance evaluation of series and parallel strategies for financial time series forecasting

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