Open AccessUncertain wind power forecasting using LSTM‐based prediction interval
Author(s) -
Banik Abhishek,
Behera Chinmaya,
Sarathkumar Tirunagaru. V.,
Goswami Arup Kumar
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.1238
Subject(s) - recurrent neural network , computer science , wind power , wind power forecasting , benchmarking , artificial neural network , prediction interval , wind speed , nonparametric statistics , data set , artificial intelligence , parametric statistics , interval (graph theory) , machine learning , data mining , electric power system , power (physics) , statistics , mathematics , engineering , meteorology , physics , quantum mechanics , marketing , electrical engineering , business , combinatorics
Estimating prediction intervals (PIs) is an efficient and reliable way of capturing the uncertainties associated with wind power forecasting. In this study, a state of the art recurrent neural network (RNN) known as long short‐term memory (LSTM) is used to produce reliable PIs for one‐hour ahead wind power uncertainty forecast using the non‐parametric lower upper bound estimation framework. Two realistic hourly stamped wind power data sets are obtained and by using mutual information and false nearest neighbours techniques, the data are made suitable for model inputs. A novel comprehensive objective function consisting of the coverage probability, the average width of the PIs, symmetricity and variational synchronicity is developed to train the LSTM model using intelligent optimisation techniques. The standard of the PIs generated for the test set as well as for different seasons are evaluated based on the indices used to design the objective function for model training, with one of them being modified. The performance of the proposed LSTM model is found to outperform typical RNN models like Elman, non‐linear auto‐regressive with exogenous models and other benchmarking models while tested on the real‐world data sets.