Can a rational expectation storage model explain the USDA ending grain stocks forecast errors?

2021 ◽  
Author(s):  
Tianyang Zhang ◽  
Ziran Li
Keyword(s):  
Rational Expectation ◽  
Forecast Errors ◽  
Storage Model

Designing the Electricity Market with Energy Storage Devices

2020 ◽  
Vol 12 (12) ◽  
pp. 31-43
Author(s):  
Tatiana A. VASKOVSKAYA ◽  
◽  
Boris A. KLUS ◽  
Keyword(s):  
Energy Storage ◽  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Energy System ◽  
Energy Storage Systems ◽  
Storage Model ◽  
Optimal Power ◽  
Wide Range

The development of energy storage systems allows us to consider their usage for load profile leveling during operational planning on electricity markets. The paper proposes and analyses an application of an energy storage model to the electricity market in Russia with the focus on the day ahead market. We consider bidding, energy storage constraints for an optimal power flow problem, and locational marginal pricing. We show that the largest effect for the market and for the energy storage system would be gained by integration of the energy storage model into the market’s optimization models. The proposed theory has been tested on the optimal power flow model of the day ahead market in Russia of 10000-node Unified Energy System. It is shown that energy storage systems are in demand with a wide range of efficiencies and cycle costs.

Storage model of servers in video grid

2010 ◽  
Vol 30 (4) ◽  
pp. 868-871
Author(s):  
Hong ZHAO ◽  
Silamu Woxur ◽  
Zheng-liang YAO ◽  
Lei YIN
Keyword(s):  
Storage Model

On the Association Between Analysts' Forecast Errors and Past Stock Returns: A Re-Examination

2002 ◽  
Author(s):  
Xia NMI Chen ◽  
Qiang Cheng
Keyword(s):  
Stock Returns ◽  
Forecast Errors

A storage model for the simulation of the hydraulic behaviour of drainage networks

1997 ◽  
Vol 36 (8-9) ◽  
pp. 57-63 ◽  
Author(s):  
Homayoun Motiee ◽  
Bernard Chocat ◽  
Olivier Blanpain
Keyword(s):  
Wave Model ◽  
Kinematic Wave ◽  
Hydraulic Simulation ◽  
Diffusion Wave ◽  
Storage Model ◽  
Hydraulic Behaviour ◽  
Drainage Networks ◽  
Saint Venant Equations ◽  
Kinematic Wave Model ◽  
And Diffusion

This paper presents a model for the hydraulic simulation of a drainage network using the storage concept. This model is easier to use than the complete Barre de Saint Venant equations and gives better results than the usual conceptual models, i.e. the Muskingum model, or than models obtained by the simplification of the Saint Venant equations (kinematic wave model and diffusion wave model).

scholarly journals A Deterministic Rapid Intensification Aid

2011 ◽  
Vol 26 (4) ◽  
pp. 579-585 ◽  
Author(s):  
Charles R. Sampson ◽  
John Kaplan ◽  
John A. Knaff ◽  
Mark DeMaria ◽  
Chris A. Sisko
Keyword(s):  
Real Time ◽  
Arithmetic Mean ◽  
Forecast Errors ◽  
Rapid Intensification ◽  
Intensity Forecast ◽  
Probability Threshold ◽  
Forecast Time ◽  
National Hurricane Center ◽  
Made In

Abstract Rapid intensification (RI) is difficult to forecast, but some progress has been made in developing probabilistic guidance for predicting these events. One such method is the RI index. The RI index is a probabilistic text product available to National Hurricane Center (NHC) forecasters in real time. The RI index gives the probabilities of three intensification rates [25, 30, and 35 kt (24 h)−1; or 12.9, 15.4, and 18.0 m s−1 (24 h)−1] for the 24-h period commencing at the initial forecast time. In this study the authors attempt to develop a deterministic intensity forecast aid from the RI index and, then, implement it as part of a consensus intensity forecast (arithmetic mean of several deterministic intensity forecasts used in operations) that has been shown to generally have lower mean forecast errors than any of its members. The RI aid is constructed using the highest available RI index intensification rate available for probabilities at or above a given probability (i.e., a probability threshold). Results indicate that the higher the probability threshold is, the better the RI aid performs. The RI aid appears to outperform the consensus aids at about the 50% probability threshold. The RI aid also improves forecast errors of operational consensus aids starting with a probability threshold of 30% and reduces negative biases in the forecasts. The authors suggest a 40% threshold for producing the RI aid initially. The 40% threshold is available for approximately 8% of all verifying forecasts, produces approximately 4% reduction in mean forecast errors for the intensity consensus aids, and corrects the negative biases by approximately 15%–20%. In operations, the threshold could be moved up to maximize gains in skill (reducing availability) or moved down to maximize availability (reducing gains in skill).

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