Fuzzy interaction regression for short term load forecasting

Hong, Tao; Wang, Pu

doi:10.1007/s10700-013-9166-9

Cited by 96 publications

(46 citation statements)

References 17 publications

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“…Based on Hong and Wang's approach [20] Hong and Wang's approach [20] is used here as it was developed to generate both linear and nonlinear fuzzy regression models. Figure 2c, which increase from levels 1 to 4 and decrease from levels 4 to 6.…”

Section: Fuzzy Regression For Aesthetic Quality Estimationmentioning

confidence: 99%

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A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Chan

Lam

Yiu

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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-Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming (GP) to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.

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Section: Fuzzy Regression For Aesthetic Quality Estimationmentioning

confidence: 99%

“…These fuzzy regression models [4,8,15,16,18,19,20,21,24 A the increasing magnitudes of objective features. The assumption may suit instrumentation measurements because the amount of uncertainty increases when measurement magnitudes increase [43].…”

mentioning

confidence: 99%

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Chan

Lam

Yiu

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…Intrusive and nonintrusive load monitoring and prediction approaches are mentioned in [18]. Fuzzy regression model is proposed by Hong and Wang [19] for STLF; the authors used historical, weekday, and temperature information for the 3-year dataset and then formulated three fuzzy linear regression models. Mean absolute percentage error (MAPE) was in the range of 2.6-4.58% for hourly peak load.…”

Section: Introductionmentioning

confidence: 99%

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

Rafique

Zhang

Rafique

et al. 2018

International Journal of Photoenergy

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The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the forecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is developed to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In the first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established coverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for renewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster are expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is carried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy management problem with more accuracy and robustness.

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“…The exponential smoothing method reported by Weron et al [23], the gray forecast method reported by Li et al [24] based on time trend extrapolation reported by Ismail et al [25], the clustering forecast method reported by Kodogiannis et al [26] and multiple regression analysis method reported by Hong et al [27] based on the load related factor analysis cannot ensure the satisfactory result in any case. In order to make full use of the advantages and the contained information of each single forecast model, combination forecast [28][29][30][31][32][33] is an effective method.…”

Section: Introductionmentioning

confidence: 99%

“…In the production process of a new particle swarm, the particle position is updated according to Equation (27), and the step length is represented by Equation (26). The coefficients of the three parts in Equation (26) are randomly changed, but only the rules of the particle to follow the order are changed.…”

Section: Introducing Disturbance Variable Into Ia-psomentioning

confidence: 99%

A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Ding

et al. 2015

Energies

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Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.

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Fuzzy interaction regression for short term load forecasting

Cited by 96 publications

References 17 publications

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

A Flexible Fuzzy Regression Method for Addressing Nonlinear Uncertainty on Aesthetic Quality Assessments

Renewable Generation (Wind/Solar) and Load Modeling through Modified Fuzzy Prediction Interval

A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

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