We consider the problem of automated anomaly detection for building level heat load time series. An anomaly detection model must be applicable to a diverse group of buildings and provide robust results on heat load time series with low signal-to-noise ratios, several seasonalities, and significant exogenous effects. We propose to employ a probabilistic forecast combination approach based on an ensemble of deterministic forecasts in an anomaly detection scheme that classifies observed values based on their probability under a predictive distribution. We show empirically that forecast based anomaly detection provides improved accuracy when employing a forecast combination approach.
翻译:我们考虑了建筑高热负荷时间序列自动异常现象探测问题。异常现象检测模型必须适用于各类建筑物,并提供关于热负荷时间序列的可靠结果,其信号到噪音比率低、若干季节性和重大外生效应。我们提议在异常现象检测计划中采用基于共同确定性预测的概率预测组合法,根据预测分布下的概率对观察到的值进行分类。我们从经验上表明,预测异常现象检测方法在使用预测组合法时可以提高准确性。