Non-intrusive estimation and prediction of residential AC energy consumption

Milan Jain, Amarjeet Singh, V. Chandan
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引用次数: 17

Abstract

Residential buildings account for a significant proportion of overall energy consumption across the world. Decentralized room level Air Conditioners (ACs) are a commonplace in developing countries such as India, contributing a major share (34% in India) of the total residential energy consumption. Option to independently control each AC presents a prime opportunity for an energy saving system. Thus, we propose PACMAN to non-intrusively (using only the temperature information) predict AC energy consumption prior to usage and estimate energy consumption post-usage. We discuss various possible applications and use cases of such feedback for the occupants. To empirically validate the performance of PACMAN, we conducted an in-situ study across seven homes in Delhi (India). We collected around 2200 hours of usage data from the different ACs, room types, and thermostat temperatures. We achieved an average accuracy of 85.3% and 83.7% with the best accuracy of 97.0% and 93.3% for the estimation and prediction of AC energy consumption respectively, across all homes. Towards the end, we discuss various outlier scenarios, opening up multiple directions for further research in this domain.
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住宅交流能耗的非侵入式估算与预测
住宅建筑占全球总能耗的很大比例。分散式房间级空调(ac)在印度等发展中国家很常见,占住宅总能耗的主要份额(印度为34%)。选择独立控制每台AC为节能系统提供了一个绝佳的机会。因此,我们建议PACMAN非侵入性地(仅使用温度信息)预测使用前的交流能耗,并估计使用后的能耗。我们讨论了各种可能的应用程序,以及为居住者提供此类反馈的用例。为了从经验上验证PACMAN的性能,我们在德里(印度)的七个家庭中进行了现场研究。我们从不同的空调、房间类型和恒温器温度中收集了大约2200小时的使用数据。对于所有家庭的交流能耗估算和预测,我们的平均准确率分别为85.3%和83.7%,最佳准确率分别为97.0%和93.3%。最后,我们讨论了各种异常情况,为该领域的进一步研究开辟了多个方向。
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