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Prediction of domestic appliances usage based on electrical consumption

机译：根据用电量预测家用电器使用情况

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Forecasting or modeling the on-off times of domestic appliances has gained increasing attention in recent years. However, comparing currently published results is difficult due to the many different data-sets and performance measures employed. In this paper, we evaluate the performance of three increasingly sophisticated approaches within a common framework on three data-sets each spanning 2 years. The approaches forecast the future on-off times of the appliances for the next 24 h on an hourly basis, solely based on historic energy consumption data. The appliances investigated are driven by user behavior and consume a significant fraction of the household’s total electrical energy consumption. We find that for all algorithms the average area under curve (AUC) in the receiver operating characteristic (ROC) is in the range between 72% and 73%, i.e. indicating mediocre prediction quality. We conclude that historic consumption data alone is not sufficient for a good quality hourly forecast.
机译：近年来，对家用电器的开关时间进行预测或建模已引起越来越多的关注。但是，由于采用了许多不同的数据集和性能指标，因此比较当前发布的结果非常困难。在本文中，我们将在一个共同的框架内，对三个跨两年的数据集评估三种日益复杂的方法的性能。这些方法仅基于历史能耗数据就可以每小时预测未来24小时内设备的未来开关时间。受调查的设备受用户行为的驱动，并且消耗了家庭总电能消耗的很大一部分。我们发现，对于所有算法，接收器工作特性（ROC）中的曲线下平均面积（AUC）在72％至73％之间，即表示中等的预测质量。我们得出结论，仅历史消费数据不足以进行高质量的小时预测。

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