Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model
نویسندگان
چکیده
منابع مشابه
Monthly streamflow forecasting using Gaussian Process Regression
Bureau of Economic Geology, Jackson School of Geosciences, University of Texas Austin, Austin, TX 78713, United States Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical, Agriculture, Chinese Academy of Sciences, Changsha, Ch...
متن کاملShort Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression
The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...
متن کاملShort-term Streamflow Forecasting: ARIMA Vs Neural Networks
Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto re...
متن کاملShort-Term Wind Energy Forecasting Using Support Vector Regression
Wind energy prediction has an important part to play in a smart energy grid for load balancing and capacity planning. In this paper we explore, if wind measurements based on the existing infrastructure of windmills in neighbored wind parks can be learned with a soft computing approach for wind energy prediction in the ten-minute to six-hour range. For this sake we employ Support Vector Regressi...
متن کاملShort Term Load Forecasting Using Multi Parameter Regression
Short Term Load forecasting in this paper uses input data dependent on parameters such as load for current hour and previous two hours, temperature for current hour and previous two hours, wind for current hour and previous two hours, cloud for current hour and previous two hours. Forecasting will be of load demand for coming hour based on input parameters at that hour. In this paper we are usi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Water Resources Management
سال: 2019
ISSN: 0920-4741,1573-1650
DOI: 10.1007/s11269-019-02399-1