Academic Journal
A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting
| العنوان: | A Robust Segmented Mixed Effect Regression Model for Baseline Electricity Consumption Forecasting |
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| المؤلفون: | Xiaoyang Zhou, Yuanqi Gao, Weixin Yao, Nanpeng Yu |
| المصدر: | Journal of Modern Power Systems and Clean Energy, Vol 10, Iss 1, Pp 71-80 (2022) |
| بيانات النشر: | IEEE, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations LCC:Renewable energy sources |
| مصطلحات موضوعية: | Segmented regression model, mixed effects, trimmed maximum likelihood, demand response, electric load, Production of electric energy or power. Powerplants. Central stations, TK1001-1841, Renewable energy sources, TJ807-830 |
| الوصف: | Renewable energy production has been surging around the world in recent years. To mitigate the increasing uncertainty and intermittency of the renewable generation, proactive demand response algorithms and programs are proposed and developed to further improve the utilization of load flexibility and increase the efficiency of power system operation. One of the biggest challenges to efficient control and operation of demand response resources is how to forecast the baseline electricity consumption and estimate the load impact from demand response resources accurately. In this paper, we propose a mixed effect segmented regression model and a new robust estimate for forecasting the baseline electricity consumption in Southern California, USA, by combining the ideas of random effect regression model, segmented regression model, and the least trimmed squares estimate. Since the log-likelihood of the considered model is not differentiable at breakpoints, we propose a new backfitting algorithm to estimate the unknown parameters. The estimation performance of the new estimation procedure has been demonstrated with both simulation studies and the real data application for the electric load baseline forecasting in Southern California. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2196-5420 |
| Relation: | https://ieeexplore.ieee.org/document/9248496/; https://doaj.org/toc/2196-5420 |
| DOI: | 10.35833/MPCE.2020.000023 |
| URL الوصول: | https://doaj.org/article/32aa0924e7ea4f53b7f1d467e1c6d410 |
| رقم الانضمام: | edsdoj.32aa0924e7ea4f53b7f1d467e1c6d410 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 21965420 |
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| DOI: | 10.35833/MPCE.2020.000023 |