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<p>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.</p>

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Smart grid electricity demand forecasting using weather-based MIDAS and machine learning models: the case of New Zealand

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月2日

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  • Rogith Ramesh Babu,
  • Nuttanan Wichitaksorn,
  • Shu Su,
  • Clarissa Cortes Pires,
  • Edna Lu

摘要

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.