Work

A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting

Year: 2018

Type: conference-paper

Abstract: Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed method utilizes multiple historical meteorological variables, such as wind speed, temperature, humidity, pressure, dew point and solar radiation to accurately predict wind speeds. The prediction performance is extensively assessed using real data collected in West Texas, USA. The experimental results show that the proposed MSLSTM can preferably capture and learn uncertainties while output competitive performance.

Source: IEEE International Conference on Big Data (BigData)

Authors: Sisheng Liang, Long Nguyen, Fang Jin

Institution: Texas Tech University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: green

Funder: National Science Foundation

Award: SCC-Planning: Enhancing Water Resource Management and Infrastructure Improvement through Sensing, Computation, and Community Engagement