Smart Grid Electricity Demand Forecasting Using Weather-based MIDAS and Machine Learning Models: The Case of New Zealand
Date
Authors
Ramesh Babu, Rogith
Wichitaksorn, Nuttanan
Su, Shu
Cortes Pires, Clarissa
Lu, Edna
Supervisor
Item type
Journal Article
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Journal Title
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Volume Title
Publisher
Springer Science and Business Media LLC
Abstract
This study develops a comparative forecasting framework that integrates daily weather information with quarterly electricity generation, used here as a proxy for electricity demand in New Zealand, through mixed-frequency modelling approaches. The analysis progresses from baseline univariate time-series models to classical mixed data sampling regressions, advanced regularised and autoregressive mixed-frequency models, and machine learning-based mixed-frequency methods. The forecasting results show that mixed-frequency models can improve upon traditional univariate benchmarks by incorporating higher-frequency weather information. Among the advanced approaches, autoregressive mixed-frequency models deliver strong forecasting performance, particularly over shorter recent evaluation windows, while seasonal time-series benchmarks such as SARIMA remain highly competitive and achieve the lowest RMSE in the main eight-quarter evaluation period. Machine learning-based mixed-frequency models show mixed performance, likely reflecting the challenges posed by data dimensionality and limited sample size. The proposed framework provides interpretable forecasts and offers practical insights for electricity system planning in renewable-dominated energy systems.Description
Keywords
Electricity demand forecasting, Mixed-frequency modelling, Weather-based forecasting, Machine learning, New Zealand energy system
Source
Japanese Journal of Statistics and Data Science, ISSN: 2520-8756 (Print); 2520-8764 (Online), Springer Science and Business Media LLC. doi: 10.1007/s42081-026-00353-1
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