Nonexclusive classification of household appliances by fuzzy deep neural networks

Fuzzy classification is a very useful tool for managing the uncertainty in a classification problem with non-mutually exclusive classes, whose values can fall into overlapping ranges. This situation is very common in real-life problems, where decisions are often made on the basis of inaccurate or noisy information and a flexible classification is preferred. In this paper, we propose a nonexclusive classification approach based on fuzzy logic to classify household appliances characterized by the time series associated with their power consumption. This issue is crucial for purposes related to user profiling, demand side management and cost optimization in the context of smart grids and green energy communities. To overcome the dependence on an expert for determining the logical rules of inference, we rely on the use of deep neural networks, as they have proved to be an extremely powerful tool in this kind of problems. The advantages and disadvantages present in fuzzy inference systems and deep neural networks almost completely disappear when both models are combined. In this regard, the paper proposes a randomization-based fuzzy deep neural network for the nonexclusive classification of household appliances. Randomization in deep neural networks allows a significant reduction in training times while often maintaining a high level of precision. This enables the adopted model with respect to time constraints causality of the observed time series. The performances obtained from the proposed model compare favorably with those obtained using two benchmark models for time series classification based on the well-known Long Short-Term Memory network.