[论文解读] Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation

Nipun Batra, Amarjeet Singh+1 more|arXiv (Cornell University)|Oct 26, 2015

Smart Grid Energy Management参考文献 20被引用 10

一句话总结

本文提出 Neighborhood NILM，一种基于大数据的家用电器能量分解方法，利用月度能耗数据及少数被子计量的‘邻近’家庭的数据，估算电器级别的能耗。通过根据房屋大小、居住人数和月度能耗趋势的相似性匹配家庭，该方法在冰箱上实现了高达83%的准确率，在洗衣机和照明设备上达到64%，优于需要高频智能电表的最先进方法。

ABSTRACT

In this paper, we investigate whether "big-data" is more valuable than "precise" data for the problem of energy disaggregation: the process of breaking down aggregate energy usage on a per-appliance basis. Existing techniques for disaggregation rely on energy metering at a resolution of 1 minute or higher, but most power meters today only provide a reading once per month, and at most once every 15 minutes. In this paper, we propose a new technique called Neighbourhood NILM that leverages data from 'neighbouring' homes to disaggregate energy given only a single energy reading per month. The key intuition behind our approach is that 'similar' homes have 'similar' energy consumption on a per-appliance basis. Neighbourhood NILM matches every home with a set of 'neighbours' that have direct submetering infrastructure, i.e. power meters on individual circuits or loads. Many such homes already exist. Then, it estimates the appliance-level energy consumption of the target home to be the average of its K neighbours. We evaluate this approach using 25 homes and results show that our approach gives comparable or better disaggregation in comparison to state-of-the-art accuracy reported in the literature that depend on manual model training, high frequency power metering, or both. Results show that Neighbourhood NILM can achieve 83% and 79% accuracy disaggregating fridge and heating/cooling loads, compared to 74% and 73% for a technique called FHMM. Furthermore, it achieves up to 64% accuracy on washing machine, dryer, dishwasher, and lighting loads, which is higher than previously reported results. Many existing techniques are not able to disaggregate these loads at all. These results indicate a potentially substantial advantage to installing submetering infrastructure in a select few homes rather than installing new high-frequency smart metering infrastructure in all homes.

研究动机与目标

• 解决在无高频智能电表的情况下家庭能量分解的挑战。
• 探究来自相似家庭的大数据是否能在能量分解准确率上超越精确的高频数据。
• 实现对低功率和复杂电器（如照明、洗碗机）的能量分解，这些电器通常被现有方法忽略。
• 通过在少数家庭中采用子计量，减少对广泛部署昂贵高频智能电表的需求。
• 开发一种可扩展的、无需模型的方法，无需手动训练电器模型。

提出的方法

• 该方法从参考数据集中识别出具有直接子计量（如电路或电器级别）的‘邻近’家庭。
• 利用月度能耗、房屋面积和居住人数等特征，将目标家庭与其K个最近邻匹配。
• 目标家庭的电器级能耗通过其K个最近邻的平均值进行估算。
• 相似性通过包含总月度能耗趋势、房屋面积和居住人数的特征向量计算。
• 该方法无需学习模型或高频功率信号，仅依赖月度账单数据和来自相似家庭的预存子计量数据。
• 通过测试不同特征组合（如原始能耗、面积、居住人数）评估特征重要性，以确定哪些特征能提升能量分解准确率。

实验结果

研究问题

• RQ1 通过利用相似家庭的数据而非依赖高频计量，能否提升能量分解的准确率？
• RQ2 来自少数子计量家庭的大数据是否足以实现对常见家用电器的高精度分解？
• RQ3 该方法能否分解通常被传统NILM技术忽略的低功率和复杂电器（如照明、洗碗机）？
• RQ4 静态家庭特征（如房屋面积、居住人数）的引入如何影响能量分解准确率？
• RQ5 随着参考数据集中子计量家庭数量的增加，该方法是否能有效扩展？

主要发现

• Neighborhood NILM 在冰箱能耗分解上达到83%的准确率，优于FHMM方法的74%。
• 其在供暖与制冷负荷上的准确率达到79%，高于FHMM的73%。
• 该方法在洗衣机、干衣机、洗碗机和照明设备上的准确率最高可达64%，高于此前针对这些电器报告的结果。
• 对于照明，房屋面积特征显著提升了准确率，可能是因为大户型拥有更多灯具。
• 对于暖通空调（HVAC），原始月度能耗趋势是最具预测性的特征，能有效捕捉季节性变化。
• 在洗衣类电器上，当将居住人数作为特征时，方法表现最佳，可能是因为其与用电频率密切相关。