Reviews on uncertainty analysis of wind power forecasting

Yan, Jie; Liu, Yongqian; Han, Soonhung; Wang, Yimei; Feng, Shuanglei

doi:10.1016/j.rser.2015.07.197

Cited by 173 publications

(71 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To increase the stability and reliability of wind power production, accurate forecasts of wind speed ramp events are therefore necessary. Generally, wind speed ramps are driven by factors at multiple temporal and spatial scales, such as the development and movement of air fronts, areas of low and high pressure, thunderstorms, boundary layer processes, mountain waves, flow channelling, and sea breeze . These features can explain why wind speed ramp events remain poorly forecasted …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of mesoscale ensemble forecast method for prediction of wind speed ramps

et al. 2019

View full text Add to dashboard Cite

Sudden changes in wind speed, so‐called wind speed ramps, are a major concern for wind power system operators. The present study applies the mesoscale ensemble forecast method for the prediction of wind speed ramps at wind farms in Japan and evaluates the ability and utility of this method. The mesoscale ensemble forecast in this study (ENS21) consists of 21 members with a horizontal resolution of 10 km for a 5‐year period. The simulated results show that ENS21 produces better accuracy than the deterministic forecast with a horizontal resolution of 10 km (DET_L). On the other hand, the deterministic forecast with a horizontal resolution of 5 km (DET_H) also produces better accuracy than DET_L. From a practical perspective, however, the ENS21 is computationally expensive. Thus, the eight‐member mesoscale ensemble forecast (ENS8) with as same computational cost as a deterministic forecast with a horizontal resolution of 5 km (DET_H) is also evaluated. The simulated results show that ENS8 has almost same accuracy as ENS21 and DET_H in wind speed ramp forecasts. ENS8 has advantages over ENS21 and DET_H because ENS8 is computationally efficient and is able to benefit wind power operators with flexibility in the selection of probability thresholds for decision processes compared with a single. It can be concluded that the mesoscale ensemble forecast method is more useful for prediction of the wind speed ramp than the single deterministic forecast method with the same computational cost if the ensemble members are successfully selected.

show abstract

Section: Introductionmentioning

confidence: 99%

“…areas of low and high pressure, thunderstorms, boundary layer processes, mountain waves, flow channelling, and sea breeze. 5,6 These features can explain why wind speed ramp events remain poorly forecasted. 7 There are two approaches for wind speed ramp forecasts.…”

mentioning

confidence: 99%

Application of mesoscale ensemble forecast method for prediction of wind speed ramps

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Distributed wind power and distributed solar photovoltaic power are two types of renewable DG, with the most mature technology and maximum commercial value in China, and have a broad prospect for development [3]. However, wind and solar photovoltaic power generation are significantly influenced by environmental and climatic factors, which makes their output strongly volatile and uncertain and has a large influence on power grid scheduling and operation [4,5]. The research conducted on this problem has mainly focused on power system day-ahead scheduling with wind and solar photovoltaic power grid connection [6,7] and ancillary service (AS) decision [8,9].…”

Section: Introductionmentioning

confidence: 99%

Joint Scheduling Optimization of Virtual Power Plants and Equitable Profit Distribution Using Shapely Value Theory

Tan

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The installation capacity of wind and solar photovoltaic power is continually increasing, which makes renewable energy grid connection and power generation an important link of China's power structure optimization. A virtual power plant (VPP) is an important way to help distributed energy resource grid connection and promote renewable energy industry development. To study the economic scheduling problem of various distributed energy resources and the profit distribution problem of VPP alliance, this study builds a separate operation scheduling model for individual VPP and a joint operation scheduling model for VPP alliance, as well as the profit distribution model. The case study verifies the feasibility and effectiveness of the proposed model. The sensitivity analysis provides information about VPP decision-making in accordance with the policy environment development trend.

show abstract

“…Jorgensen and Sjoberg [29] used the nonparametric histogram method to find the points of the software development workload distribution. Yan et al [30], thought that the errors of point forecasts have great influence on the accuracy of uncertainty analysis. Ma et al [31] investigated existence and the local stable region of the Nash equilibrium point.…”

Section: Introductionmentioning

confidence: 99%

The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

Wang

Guo

2016

Entropy

View full text Add to dashboard Cite

Abstract:With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers' demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interval and propose a practical and efficient algorithm based on entropy theory and complexity theory. In order to improve the estimation precision of the error distribution, the point prediction errors are STRATIFIED according to prices and the complexity of the system; the corresponding prediction error samples are obtained by the prices stratification; and the error distributions are estimated by the kernel function method and the stability of the system. In a stable and orderly environment for price forecasting, we obtain point prediction error samples by the weighted local region and RBF (Radial basis function) neural network methods, forecast the intervals of the soybean meal and non-GMO (Genetically Modified Organism) soybean continuous futures closing prices and implement unconditional coverage, independence and conditional coverage tests for the simulation results. The empirical results are compared from various interval evaluation indicators, different levels of noise, several target confidence levels and different point prediction methods. The analysis shows that the optimal interval construction method is better than the equal probability method and the shortest interval method and has good anti-noise ability with the reduction of system entropy; the hierarchical estimation error method can obtain higher accuracy and better interval estimation than the non-hierarchical method in a stable system.

show abstract

Reviews on uncertainty analysis of wind power forecasting

Cited by 173 publications

References 48 publications

Application of mesoscale ensemble forecast method for prediction of wind speed ramps

Application of mesoscale ensemble forecast method for prediction of wind speed ramps

Joint Scheduling Optimization of Virtual Power Plants and Equitable Profit Distribution Using Shapely Value Theory

The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

Contact Info

Product

Resources

About