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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands.

Faisal Mehmood Butt1,2, Lal Hussain3,4, Anzar Mahmood1

  • 1Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur 10250, Azad Kashmir, Pakistan.

Mathematical Biosciences and Engineering : MBE
|February 2, 2021
PubMed
Summary

Accurate electrical load forecasting using Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN) improves power grid management. These models show stable performance for short-term load forecasting (STLF) and medium-term load forecasting (MTLF).

Keywords:
Short-term load forecastartificial intelligenceconvolutional neural networksdeep neural networkslong short-term memory networks

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Time Series Analysis

Background:

  • Efficient power company management and scheduling rely on accurate electrical load forecasting.
  • Uncertainties in load time series present challenges for short-term load forecast (STLF), medium-term load forecast (MTLF), and long-term load forecast (LTLF).
  • Accurate forecasting is crucial for grid stability, resource allocation, and operational efficiency.

Purpose of the Study:

  • To propose and evaluate advanced neural network models for improved electrical load forecasting accuracy.
  • To extract local trends and capture patterns in short and medium-term load time series.
  • To validate the stability and practicality of proposed models using real-world data.

Main Methods:

  • Implemented and compared Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) models.
  • Trained models on real-world electrical load data to learn time series relationships.
  • Evaluated model performance using metrics such as R-squared, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Main Results:

  • LSTM, MLP, and CNN models demonstrated stable and improved performance across various forecasting horizons (24 hours, 72 hours, 1 week, 1 month).
  • Specific models excelled at different time scales: LSTM and MLP for 24-hour and 72-hour forecasts, CNN and MLP for 1-week forecasts, and CNN, MLP, and LSTM for 1-month forecasts.
  • Lowest prediction errors were achieved with LSTM (R2: 0.5160 for 24h), MLP (MAPE: 4.97 for 24h), CNN (R2: 0.7616 for 1 week), and CNN (R2: 0.820 for 1 month).

Conclusions:

  • The proposed neural network models (LSTM, MLP, CNN) offer significant improvements in electrical load forecasting accuracy and stability.
  • Findings suggest STLF models are suitable for local system planning and dispatch.
  • MTLF models are efficient for enhanced scheduling and maintenance operations in power systems.