Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory

Tang, Lei; Wang, Xifan; Wang, Xiuli; Shao, Chengcheng; Liu, Shiyu; Tian, Shijun

doi:10.1016/j.energy.2018.10.073

Cited by 82 publications

(31 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recently published paper utilized fuzzy Bayesian theory and expert predictions for LTLF. 11 The presented technique is able to predict the 10 years ahead Chinese per-capita electricity consumption. The main challenge towards MTLF and LTLF in particular is the data availability, where LTLF demands huge yearly data of a residential building, organization, and even a town or country.…”

Section: Cons Of Employed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

et al. 2021

View full text Add to dashboard Cite

Section: Cons Of Employed Methodsmentioning

confidence: 99%

“…Similarly, LTLF is a difficult task, as it involves yearly training data for future prediction. A recently published paper utilized fuzzy Bayesian theory and expert predictions for LTLF 11 . The presented technique is able to predict the 10 years ahead Chinese per‐capita electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

et al. 2021

View full text Add to dashboard Cite

“…Due to the nonlinearity, time-varying and uncertainty of, in recent years, the focus of electricity consumption forecasting research has shifted from the traditional methods that were mentioned above to emerging nonlinear methods. e common nonlinear methods that are used by scholars include the gray-forecasting model [13,24], the genetic algorithm [25,26], and the neural network model [27,28], in addition to more complex probability forecasting model [29,30] and the combination method [31,32]. In gray theory, we need to address the unknown gray information.…”

Section: Forecast Methods For Electricity Consumptionmentioning

confidence: 99%

Study of the Modified Logistic Model of Chinese Electricity Consumption Based on the Change of the GDP Growth Rate under the Economic New Normal

Cui

Zhao

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the context of the new normal, the global economy is entering a deep adjustment period, and the driving forces of development are also constantly changing. As a result, China’s economy has also entered a “new normal” phase in which it is growing in a manageable and relatively balanced manner. In addition, the new normal characteristics of the power industry’s development in China are also very significant, and they affect the adaptability of traditional power forecasting methods. By analyzing the new characteristics of China’s economic development and the changing electricity demand in recent years, this paper quantitatively studied the effect of the national economy on the consumption of electricity. Meanwhile, a modified logistic model based on the change of the gross domestic product (GDP) growth rate is constructed to make a reasonable prediction of the future power consumption in China. Subsequently, by calculating the elasticity of electricity consumption in the future, it is found that the coefficient decreases each year, which indicates that electricity consumption in China is following a new trend. Based on the research results, this paper proposes rational suggestions for China’s power development, and they are expected to provide references for the power planning and power industry layout in China.

show abstract

“…Cerne et al proposed a shortterm load forecasting method by separating daily profiles and using a single fuzzy model across the entire domain [22]. Tang et al introduced a long-term electricity consumption forecasting method based on expert prediction and fuzzy Bayesian theory [23]. Jamaaluddin and Robandi applied a short-term load forecasting using hybrid regression and interval Type-1 fuzzy inference [24].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Load Forecast with Optimized Parametric Adjustment Using Jaya Optimization Algorithm

Ánh¹

2020

IJCIS

View full text Add to dashboard Cite

This paper proposes an advanced fuzzy load forecast method optimized by modified Jaya optimization (MJO) algorithm. MAT-LAB® platform is used to implement the proposed hybrid Fuzzy-MJO load forecasting algorithm and to verify the outperforming features of a Jaya technique over a fuzzy load forecast model. The novel Fuzzy-MJO load forecasting systems uses the daytime and the daily power consumption to efficiently predict the forecast power consumption. The comparative load forecasting results between proposed Fuzzy-MJO with the latest other algorithms are adequately presented. The full week forecast results using proposed hybrid Fuzzy-MJO load forecasting algorithm demonstrates an outperforming superiority, through the various tested cases, regarding to the total and the peak power error in comparison with the fuzzy-based load forecast model.

show abstract

Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory

Cited by 82 publications

References 29 publications

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Study of the Modified Logistic Model of Chinese Electricity Consumption Based on the Change of the GDP Growth Rate under the Economic New Normal

Fuzzy Load Forecast with Optimized Parametric Adjustment Using Jaya Optimization Algorithm

Contact Info

Product

Resources

About