The Experts below are selected from a list of 29085 Experts worldwide ranked by ideXlab platform
Hossein Hassani - One of the best experts on this subject based on the ideXlab platform.
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Singular Spectrum Analysis: Methodology and Comparison
2007Co-Authors: Hossein HassaniAbstract:In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA tech- nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com- pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more Accurate Forecast than the other methods indicated above.
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Singular Spectrum Analysis: Methodology and Comparison
2007Co-Authors: Hossein HassaniAbstract:Abstract: In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA tech-nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com-pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more Accurate Forecast than the other methods indicated above. Key words: ARAR algorithm, Box-Jenkins SARIMA models, Holt-Winter algorithm, singular spectrum analysis (SSA), USA monthly accidental deaths series. 1
Sue Ellen Haupt - One of the best experts on this subject based on the ideXlab platform.
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short term wind Forecast of a data assimilation weather Forecasting system with wind turbine anemometer measurement assimilation
Renewable Energy, 2017Co-Authors: William Y Y Cheng, Alfred J Bourgeois, Yonghui Wu, Sue Ellen HauptAbstract:Abstract In recent years, adopting renewable energy, such as wind power, has become a national energy policy for many countries due to concerns of pollution and climate change from fossil fuel consumption. However, Accurate prediction of wind is crucial in managing the power load. Numerical weather prediction (NWP) models are essential tools for wind prediction, but they need Accurate initial conditions in order to produce an Accurate Forecast. However, NWP models are not guaranteed to have Accurate initial conditions over wind farms in isolated locations. This study hypothesizes that short-term, 0–3 h, wind Forecast can be improved by assimilating anemometer wind speed observations from wind farm turbines into a numerical weather Forecast system. A technique was developed to circumvent the requirement of simultaneously ingesting the wind speed and direction in a data assimilation/weather Forecasting system. A six-day case study revealed that assimilating wind speed can improve the 0–3 h wind speed (power) Forecast by reducing the mean absolute error up to 0.5–0.6 m s−1 (30–40%).
L P Perez - One of the best experts on this subject based on the ideXlab platform.
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application of support vector machines and anfis to the short term load Forecasting
IEEE PES Transmission and Distribution Conference and Exposition, 2008Co-Authors: A M Escobar, L P PerezAbstract:Load Forecasting is vitally important for the electric industry in the deregulated economy. Short-term load Forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, Accurate Forecast of electricity load has received increasing attention. However, Forecasting electricity load is difficult because of the randomness and uncertainties of load demand. Many mathematical methods have been developed for load Forecasting. In this paper we discuss some methodologies for load Forecasting. One set of load Forecasting curves are used for make a classification with different techniques as neural networks, fuzzy logic and support vector machines. A comparative analysis is done for each technique and the results present the advantages of each one of them.
Fu Xiao-ke - One of the best experts on this subject based on the ideXlab platform.
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Short-term Load Forecasting Using Pattern Recognition and Neural Network
Journal of Nanchang University, 2003Co-Authors: Fu Xiao-keAbstract:Short term load Forecasting (STLF) for a lead time of one hour to 24 hours is essential for planning start up and shut-down schedules of generating units,overhaul planning and load management.An Accurate Forecast eases the problem of generation and load management to a great extent.This paper presents an application of ANN based on basic principles of pattern recognition to short term load Forecasting.
William Y Y Cheng - One of the best experts on this subject based on the ideXlab platform.
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short term wind Forecast of a data assimilation weather Forecasting system with wind turbine anemometer measurement assimilation
Renewable Energy, 2017Co-Authors: William Y Y Cheng, Alfred J Bourgeois, Yonghui Wu, Sue Ellen HauptAbstract:Abstract In recent years, adopting renewable energy, such as wind power, has become a national energy policy for many countries due to concerns of pollution and climate change from fossil fuel consumption. However, Accurate prediction of wind is crucial in managing the power load. Numerical weather prediction (NWP) models are essential tools for wind prediction, but they need Accurate initial conditions in order to produce an Accurate Forecast. However, NWP models are not guaranteed to have Accurate initial conditions over wind farms in isolated locations. This study hypothesizes that short-term, 0–3 h, wind Forecast can be improved by assimilating anemometer wind speed observations from wind farm turbines into a numerical weather Forecast system. A technique was developed to circumvent the requirement of simultaneously ingesting the wind speed and direction in a data assimilation/weather Forecasting system. A six-day case study revealed that assimilating wind speed can improve the 0–3 h wind speed (power) Forecast by reducing the mean absolute error up to 0.5–0.6 m s−1 (30–40%).