Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Kristen R. Schell"'
Autor:
Haolin Yang, Kristen R. Schell
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 158, Iss , Pp 109975- (2024)
The primary contribution of this study is the proposal of an explainable deep-learning neural network (ATTnet) that employs an attention mechanism to achieve accurate electricity spot price forecasting and an explainable model pipeline. The concise,
Externí odkaz:
https://doaj.org/article/d9b1e52d9b2540c3ac16855abdfe95a0
Publikováno v:
Renewable Energy. 211:285-295
Publikováno v:
International Journal of Forecasting. 38:300-320
Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatia
Infomorphism: Urban Planning For Renewable Energy Integration Via Simulated Energy Exchange Networks
Publikováno v:
2022 Annual Modeling and Simulation Conference (ANNSIM).
Autor:
Haolin Yang, Kristen R. Schell
Publikováno v:
International Journal of Electrical Power & Energy Systems. 141:108092
Autor:
Haolin Yang, Kristen R. Schell
Publikováno v:
2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).
Electricity price forecasting is critical to numerous tasks in the power system such as strategic bidding, generation scheduling, optimal scheduling of storage reserves and system analysis. Most existing price forecasting models focus on hourly predi
Autor:
Haolin Yang, Kristen R. Schell
Publikováno v:
Energy. 238:122052
A highly accurate electricity price prediction model is of the utmost importance for multiple power systems tasks, such as generation dispatch and bidding. Due to the liberalization of the electricity market, as well as high renewable penetration, th
Publikováno v:
Electric Power Systems Research. 165:45-52
In this paper we bring together a stochastic mixed integer programming model for transmission network expansion planning, incorporating portfolios of real options to address the evolution in time of uncertain parameters, with the adjusted generalized
Autor:
Haolin Yang, Kristen R. Schell
Publikováno v:
Applied Energy. 299:117242
The ability to forecast real-time electricity price for wind power is key to the operation of energy markets and hedging price risks. Recent research suggests new deep neural network (DNN) architectures can capture temporal dependencies in historical
Publikováno v:
International Journal of Electrical Power & Energy Systems. 90:1-9
It is estimated that Europe alone will need to add over 250,000 km of transmission capacity by 2050, if it is to meet renewable energy production goals while maintaining security of supply. Estimating the cost of new transmission infrastructure is di