A Day-ahead Wind Speed Prediction based on Meteorological Data and the Seasonality of Weather Fronts

被引：0

作者：

Finamore, Antonella ^{[1
]}

Calderaro, Vito ^{[1
]}

Galdi, Vincenzo ^{[1
]}

Piccolo, Antonio ^{[1
]}

Conio, Gaspare ^{[2
]}

机构：

[1] Univ Salerno, DIIn, Fisciano, SA, Italy

[2] Italian Vento Power Corp SERV SRL, Avellino, Italy

来源：

2019 IEEE PES GTD GRAND INTERNATIONAL CONFERENCE AND EXPOSITION ASIA (GTD ASIA) | 2019年

关键词：

Data mining; meteorological information; neural networks; renewable energy; wind speed forecasting;

D O I：

10.1109/gtdasia.2019.8715985

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A reliable and accurate forecasting model is one of the most effective solutions to deal with the problem of renewable energy sources integration. In this paper, a model for the medium-long-term wind speed prediction, based on spatiotemporal evolution of weather fronts and Multi-Layer Perceptron Neural Network (MLP NN) data mining model, is developed. The model inputs are the historical and current meteorological data, such as pressure, temperature and wind intensity. These data describe the evolution of the weather fronts in a wide area around the point of interest, which goes beyond the local bounds. The model, trained and tested using real weather data, predicts the 24-h ahead wind speed. Forecasted results are compared with real data registered in the test site. This comparison demonstrates the efficiency and the effectiveness of the proposed strategy.

引用

页码：915 / 920

页数：6

共 50 条

[21] A Hybrid Forecasting Model Based on Artificial Neural Network and Teaching Learning Based Optimization Algorithm for Day-Ahead Wind Speed Prediction
Santhosh, Madasthu
Venkaiah, Chintham
Kumar, D. M. Vinod
INTELLIGENT COMPUTING TECHNIQUES FOR SMART ENERGY SYSTEMS, 2020, 607 : 455 - 463
[22] Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM
Qing, Xiangyun
Niu, Yugang
ENERGY, 2018, 148 : 461 - 468
[23] A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique
Abbasipour, Mehdi
Igder, Mosayeb Afshari
Liang, Xiaodong
IEEE ACCESS, 2021, 9 : 151142 - 151154
[24] Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm
Qin, Guangyu
Yan, Qingyou
Zhu, Jingyao
Xu, Chuanbo
Kammen, Daniel M.
SUSTAINABILITY, 2021, 13 (03) : 1 - 18
[25] Day-ahead wind speed forecasting using f-ARIMA models
Kavasseri, Rajesh G.
Seetharaman, Krithika
RENEWABLE ENERGY, 2009, 34 (05) : 1388 - 1393
[26] Comparison of Three Methods for a Weather Based Day-Ahead Load Forecasting
Zou, Mingzhe
Gu, Jiachen
Fang, Duo
Harrison, Gareth
Djokic, Sasa
Wang, Xinying
Zhang, Chen
PROCEEDINGS OF 2019 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT-EUROPE), 2019,
[27] Multi-step-ahead Method for Wind Speed Prediction Correction Based on Numerical Weather Prediction and Historical Measurement Data
Wang, Han
Yan, Jie
Liu, Yongqian
Han, Shuang
Li, Li
Zhao, Jing
WINDEUROPE CONFERENCE & EXHIBITION 2017, 2017, 926
[28] A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey
Ozen, Cem
Deniz, Ali
WIND ENGINEERING, 2022, 46 (05) : 1359 - 1388
[29] One Hour Ahead Prediction of Wind Speed Based on Data Mining
Liu Dejun
Li Hui
Ma Zhonghua
2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC 2010), VOL. 5, 2010, : 199 - 203
[30] Day-ahead Electricity Price Prediction Based on Multiple ELM
Tian, Huixin
Meng, Bo
Wang, ShuZhou
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, : 241 - 244

← 1 2 3 4 5 →