Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings

Chae Young Tae; Horesh Raya; Hwang Youngdeok; Lee Young M.

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Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings

机译：人工神经网络模型预测商业建筑每小时的用电量

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摘要

Short-term load forecasting of building electricity usage is of great importance for anomaly detection on electricity usage pattern and management of building energy consumption in an environment where electricity pricing is dynamically determined based on the peak energy consumption. In this paper, we present a data-driven forecasting model for day-ahead electricity usage of buildings in 15-minute resolution.

机译：在基于峰值能耗动态确定电价的环境中，建筑用电量的短期负荷预测对于用电量模式的异常检测和建筑能耗的管理至关重要。在本文中，我们提出了一种数据驱动的预测模型，用于以15分钟的分辨率对建筑物的日用电量进行预测。

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来源
《Energy and Buildings》 |2016年第1期|184-194|共11页
作者
Chae Young Tae; Horesh Raya; Hwang Youngdeok; Lee Young M.;
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作者单位

Cheongju Univ, Dept Architectural Engn, 298 Daesung Ro, Cheongju, South Korea;

IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY USA;

IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY USA;

IBM Corp, Thomas J Watson Res Ctr, Yorktown Hts, NY USA;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Building electricity consumption; Short-term load forecasting; Artificial Neural Network; Bayesian regularization;

机译：建筑耗电量;短期负荷预测;人工神经网络;贝叶斯正则化;

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Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings

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