Optimized statistical test for event detection in non-intrusive load monitoring

Abstract

Event detection plays an important role in nonintrusive load monitoring to accurately detect when appliances are switched on or off in a residential environment. Besides being accurate, it is important that these methods are robust on real-life power traces. This paper shows that some state-of-the-art event detection methods may miss events when there is a substantial base load caused by active power consuming devices. In order to address this problem, this paper extends the existing chi-squared goodness-of-fit test with a a voting scheme. Furthermore, a work flow is proposed using surrogate-based optimisation for tuning the parameters of these tests in an efficient way. Results on the BLUED dataset indicate that the novel voting chi-squared GOF method outperforms the standard chi-squared GOF test when applied to traces with a higher base load.

Adaptive threshold event detection method based on standard deviation

“…In addition, many scholars use probabilistic model-based approaches for event detection. For example, the generalized likelihood ratio method [18,23,24], goodness-of-fit test method [25][26][27], cumulative sum (CUSUM) test method [28], among others. Others have improved the performance of event detection algorithms by integrating multiple algorithms.…”