The Neural Energy Decoder Energy Disaggregation by Combining Binary Subcomponents
In this paper a novel approach for energy disag- gregation is introduced that identifies additive subcomponents of the signal in an unsupervised way. In a subsequent step, combinations of these subcomponents are sought that constitute appliances. Once the subcomponents that constitute an appliance are identified, energy disaggregation can be viewed as non- linear filtering of high frequency current readings. The approach introduced here tries to avoid numerous pitfalls of existing energy disaggregation techniques such as computational complexity issues, data transmission limitation or unrealistic assumptions about prior knowledge of appliances. The proposed method is used to infer the states of appliances in the BLUED dataset.