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Long, short, and medium terms wind speed prediction model based on LSTM optimized by improved moth flame optimization

Runze Li1, Jianzhou Wang2, Jingrui Li1

  • 1Institute of Systems Engineering, Macau University of Science and Technology, Macau, 999078, China.

Environmental Science and Pollution Research International
|May 21, 2024
PubMed
Summary
This summary is machine-generated.

Accurate wind speed prediction is vital for stable wind power generation. This study introduces an optimized Long Short-Term Memory (LSTM) system using an improved Moth Flame Optimization algorithm, enhancing prediction accuracy for wind energy systems.

Keywords:
Fuzzy information granulationImproved moth flame optimization algorithmLong and short terms memory networkWind speed prediction

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Time Series Forecasting

Background:

  • Wind speed volatility poses significant risks to wind turbines and power grids, necessitating precise prediction methods.
  • Accurate wind speed forecasting is critical for reliable wind power generation and grid stability.
  • Existing prediction models often struggle with the inherent instability of wind speed data.

Purpose of the Study:

  • To develop an enhanced wind speed prediction system for improved wind power generation.
  • To optimize the Long Short-Term Memory (LSTM) network using a novel swarm intelligence algorithm.
  • To evaluate the performance of the proposed system in short-term, medium-term, and long-term wind speed forecasting.

Main Methods:

  • Implemented a three-module system: data preprocessing, optimization, and prediction.
  • Utilized fuzzy information granulation for data preprocessing to handle input data ambiguity.
  • Employed an improved Moth Flame Optimization (MFO) algorithm to tune the LSTM prediction model parameters.
  • Applied the optimized LSTM model to predict wind speed time series.

Main Results:

  • The proposed system achieved a Mean Absolute Percentage Error (MAPE) of 3.15% for short-term forecasts.
  • Medium-term forecasts yielded a MAPE of 4.38%, demonstrating robust performance.
  • Long-term forecasts resulted in a MAPE of 18.28%, indicating effectiveness across different time horizons.
  • The experimental results showed significant advantages over previous wind speed prediction models.

Conclusions:

  • The improved Moth Flame Optimization algorithm effectively optimizes the LSTM network for wind speed prediction.
  • The proposed system demonstrates superior accuracy and reliability in forecasting wind speed across various time scales.
  • This approach offers a promising solution for enhancing the stability and efficiency of wind power generation.