NDLI: Demystifying the use of ERA5-land and machine learning for wind power forecasting

Content Provider	IET Digital Library
Author	Marinšek, Alexander Bajt, Gregor
Abstract	Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open-source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short-term memory (LSTM) for short-term wind power forecasting based on the publicly accessible ERA5-Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3-hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.
Starting Page	4159
Ending Page	4168
Page Count	10
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 19, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2020.0576
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/19
Journal	IET Renewable Power Generation
Publisher Date	2020-12-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Baseline Model Forecasts Climate Data Data Grid Points Data Structure Data Structures Deep Learning (artificial Intelligence) Deep Learning Model Electric Grid Energy Storage ERA5-Land Data File Organisation Hyperparameter Selection Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Load Forecasting Long Short-term Memory Low Investment Cost Machine Learning Nominal Power Open Climate Data Open-source Forecast Model Power Engineering Computing Power Grid Power Production Power System Planning And Layout Power System Simulation Probability Theory Random Forest Random Forest Regression Reduced Forecast Duration Regression Analysis Renewable Energy Source Short-term Memory Short-term Wind Power Forecasting Statistics Stochastic Linearised SCUC Support Vector Machine Support Vector Regression Surplus Energy Time 1.0 Hour to 24.0 Hour Wind Inconsistency Wind Power Wind Power Plant Wind Turbine WT Power Output Forecast WT Power Output Prediction Error
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast

Short-term wind power forecasting and uncertainty analysis using a hybrid intelligent method

Short-term wind power forecasting based on two-stage attention mechanism

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Multi-step wind power forecast based on VMD-LSTM

A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method

Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression

Demystifying the use of ERA5-land and machine learning for wind power forecasting

Similar Documents

Support-Vector-Machine-Enhanced Markov Model for Short-Term Wind Power Forecast

Short-term wind power forecasting and uncertainty analysis using a hybrid intelligent method

Short-term wind power forecasting based on two-stage attention mechanism

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Multi-step wind power forecast based on VMD-LSTM

A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting

Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory

Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method

Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression

Demystifying the use of ERA5-land and machine learning for wind power forecasting