scholarly journals Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

2004 ◽  
Author(s):  
Tilmann Gneiting ◽  
Kristin Larson ◽  
Kenneth Westrick ◽  
Marc G. Genton ◽  
Eric Aldrich
Keyword(s):  
Wind Energy ◽  
Regime Switching ◽  
Space Time ◽  
Probabilistic Forecasting ◽  
Energy Center

Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center

2006 ◽  
Vol 101 (475) ◽  
pp. 968-979 ◽  
Author(s):  
Tilmann Gneiting ◽  
Kristin Larson ◽  
Kenneth Westrick ◽  
Marc G Genton ◽  
Eric Aldrich
Keyword(s):  
Wind Energy ◽  
Probabilistic Forecasting ◽  
Energy Center

Day-ahead Load Probabilistic Forecasting Based on Space-time Correction

2020 ◽  
Vol 13 ◽  
Author(s):  
Fei Jin ◽  
Xiaoliang Liu ◽  
Fangfang Xing ◽  
Guoqiang Wen ◽  
Shuangkun Wang ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Northern China ◽  
Space Time ◽  
Load Prediction ◽  
Probabilistic Forecasting ◽  
Residual Distribution ◽  
Time And Space ◽  
Probability Prediction ◽  
Time Correction ◽  
Interval Coverage

Background : The day-ahead load forecasting is an essential guideline for power generating, and it is of considerable significance in power dispatch. Objective: Most of the existing load probability prediction methods use historical data to predict a single area, and rarely use the correlation of load time and space to improve the accuracy of load prediction. Methods: This paper presents a method for day-ahead load probability prediction based on space-time correction. Firstly, the kernel density estimation (KDE) is employed to model the prediction error of the long short-term memory (LSTM) model, and the residual distribution is obtained. Then the correlation value is used to modify the time and space dimensions of the test set's partial period prediction values. Results: The experiment selected three years of load data in 10 areas of a city in northern China. The MAPE of the two modified models on their respective test sets can be reduced by an average of 10.2% and 6.1% compared to previous results. The interval coverage of the probability prediction can be increased by an average of 4.2% and 1.8% than before. Conclusion: The test results show that the proposed correction schemes are feasible.

scholarly journals Comparison of the economic impact of different wind power forecast systems for producers

2014 ◽  
Vol 11 (1) ◽  
pp. 49-53 ◽  
Author(s):  
S. Alessandrini ◽  
F. Davò ◽  
S. Sperati ◽  
M. Benini ◽  
L. Delle Monache
Keyword(s):  
Wind Energy ◽  
Wind Power ◽  
Economic Benefit ◽  
Wind Farm ◽  
Probabilistic Approach ◽  
Forecast Errors ◽  
Energy Prices ◽  
Probabilistic Forecasting ◽  
Wind Power Forecast ◽  
The Future

Abstract. Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system.

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