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An Insight of Deep Learning Based Demand Forecasting in Smart Grids
Javier Manuel Aguiar-Pérez1, María Ángeles Pérez-Juárez1
1Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, Spain.
Sensors (Basel, Switzerland)
|February 11, 2023
Summary
Deep Learning models, particularly Long Short-Term Memory networks, are crucial for accurate energy demand forecasting in smart grids. These advanced techniques help balance electricity supply and demand for an efficient power system.
Area of Science:
- Electrical Engineering
- Computer Science
- Data Science
Background:
- Smart grids generate vast amounts of data, necessitating advanced methods for consumption pattern analysis.
- Accurate energy demand forecasting is vital for balancing electricity supply and demand in modern power systems.
Purpose of the Study:
- To highlight the importance of demand forecasting in smart grids.
- To explore the application of Deep Learning techniques for energy demand prediction.
Main Methods:
- Utilizing data-driven techniques to analyze smart grid data.
- Employing Deep Learning models, specifically Long Short-Term Memory (LSTM) networks, for pattern recognition and forecasting.
Main Results:
- Deep Learning models demonstrate effectiveness in learning patterns from customer consumption data.
- LSTM networks show prominence in forecasting energy demand across various horizons.
Conclusions:
- Accurate short-term load forecasting is critical for efficient power system operation and demand response.
- Continued research and industry effort in Deep Learning for demand forecasting are essential.
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