Enhancing Electricity Price Forecasting Accuracy in Smart Grids using Robust Big Data Analytics

Abstract

Accurate electricity price forecasting is crucial for efficient smart grid management. This research investigates the application of robust big data analytics techniques to improve the accuracy and reliability of short-term electricity price forecasting. We leverage advanced machine learning models trained on large, diverse datasets encompassing weather patterns, demand profiles, generation capacity, and market dynamics. The results demonstrate a significant improvement in forecasting accuracy compared to traditional methods, contributing to enhanced grid stability, optimized resource allocation, and reduced operational costs. This improved accuracy enables proactive grid management and more effective energy trading strategies.

Introduction

The increasing integration of renewable energy sources and the growth of demand-side management in smart grids necessitate accurate and reliable electricity price forecasting. Precise price predictions are essential for efficient energy trading, optimal scheduling of generation units, and effective demand-response programs. Existing forecasting methods often struggle with the volatility of renewable energy generation and the complexity of market dynamics. This research addresses these challenges by exploring the potential of big data analytics to enhance the accuracy and robustness of electricity price forecasting. A key gap lies in developing models resilient to noise and outliers inherent in large, heterogeneous datasets.

Objectives

• To develop a robust big data analytics framework for short-term electricity price forecasting.
• To improve the accuracy and reliability of electricity price predictions compared to existing methods.
• To evaluate the performance of different machine learning models for this specific application.