Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques
نویسندگان
چکیده
منابع مشابه
Electricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملElectricity Load Forecasting Using Machine Learning Techniques
Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical ...
متن کاملCorrelation and instance based feature selection for electricity load forecasting
Appropriate feature (variable) selection is crucial for accurate forecasting. In this paper we consider the task of forecasting the future electricity load from a time series of previous electricity loads, recorded every 5 minutes. We propose a two-step approach that identifies a set of candidate features based on the data characteristics and then selects a subset of them using correlation and ...
متن کاملMachine learning based switching model for electricity load forecasting
In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts...
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: Energy
سال: 2015
ISSN: 0360-5442
DOI: 10.1016/j.energy.2015.04.039