Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis

TL;DR

This paper presents new real-time energy price datasets from New Zealand, enabling advanced streaming analysis and forecasting, and demonstrates their utility through extensive experiments addressing various challenges.

Contribution

Introduction of novel real-time energy price datasets for streaming regression tasks and comprehensive analysis of their applications and challenges.

Findings

Datasets effectively support streaming regression and anomaly detection.

Preprocessing and concept drift detection are crucial for accurate forecasting.

Challenges include handling concept drift and data anomalies.

Abstract

This paper introduces a group of novel datasets representing real-time time-series and streaming data of energy prices in New Zealand, sourced from the Electricity Market Information (EMI) website maintained by the New Zealand government. The datasets are intended to address the scarcity of proper datasets for streaming regression learning tasks. We conduct extensive analyses and experiments on these datasets, covering preprocessing techniques, regression tasks, prediction intervals, concept drift detection, and anomaly detection. Our experiments demonstrate the datasets' utility and highlight the challenges and opportunities for future research in energy price forecasting.