Smart Grid Knowledge Representation and Reasoning Based on Adaptive Neuro-Fuzzy Inference System

Several models have been created for Smart Grid resource-allocation problem. The principal purpose of the models is to connect power sources with appropriate sinks when considering the input parameters of power balance and consumption size, etc. Fuzzy logic is representative of these models. When creating the fuzzy model, the parameters and rule construction play the most significant role. For the fuzzy Logic model, the rule base has been constructed considering the operator’s general knowledge, the operator’s activity and the experience. However, the fuzzy model did not have any clear boundary for the price, power, and distance values, so the model output was largely dependent on the contributions from individual portions. This paper introduces an Adaptive Neuro Fuzzy Inference System (ANFIS) approach to the smart grid problem. Learning is another attribute that can be incorporated into the current model. ANFIS is one way that the current model can be extended to. In ANFIS, the learning is done with the incorporation of a neural network. The Fuzzy system is trained over time to become self-adaptive. Once the training is complete, the system is capable of making intelligent changes based on a neural network. As a result, ANFIS has a distinct boundary for each segment. Hence, a little change along the boundary value pushed the value into the next segment.