MMDS大阪大学 数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Forecasting High Frequency Electricity Demand using Temperature in Australian Electricity Market

Katja Ignatieva (UNSW Australia, Business School)

大阪大学 金融・保険セミナーシリーズ 第107回

Forecasting High Frequency Electricity Demand using Temperature in Australian Electricity Market

Katja Ignatieva (UNSW Australia, Business School)

This paper introduces a Generalised Additive Model (GAM) to link high frequency (5-minute) aggregate electricity demand in Australia to the time of the day, time of the year and intra-day temperature. We document a strong relationship between high frequency electricity demand and intra-day temperature. We show a superior model fit when using Daylight Saving Time (DST), or clock time, instead of the standard (solar) time. We introduce the time weighted temperature model that captures instantaneous electricity demand sensitivity to temperature as a function of the human daily activity cycle, by assigning different temperature signal weighting based on the DST time. Using yearly and seasonal models, we document excellent accuracy when evaluating forecasting performance, which results in small forecasting errors. The results on DST and time weighted temperature modelling are novel in the literature and are important innovations in high frequency electricity demand forecasting.

講師: Katja Ignatieva (UNSW Australia, Business School)
テーマ: 大阪大学 金融・保険セミナーシリーズ 第107回
日時: 2019年07月08日(月) 16:20-17:50
場所: 基礎工学研究科I棟2階 I204教室
参加費: 無料
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