adaptive neural fuzzy inference system(anfis)

The energy consumption of a residential building is considered in terms of energy use and efficiency. Therefore, forecasting the energy consumption of buildings has been raised as a challenge in recent decades. In a residential home, electricity consumption can have recognizable patterns daily, monthly, or yearly depending on living conditions and daily habits and events. In this research, artificial neural network (ANN) and adaptive fuzzy-neural inference system (ANFIS) have been performed using MATLAB software to predict building energy consumption. Also, random data collected based on the criteria obtained from the hourly electricity consumption of conventional residential buildings in Tehran has been used. In order to evaluate and measure the performance of this model, statistical indicators have been used. According to the applied settings (type of learning, number of steps, and error tolerance), the system error rate is calculated based on MSE, RMSE, μ, σ, and R statistical indicators and the results of energy consumption forecast in three buildings with high accuracy and correlation coefficient. R is more than 98%. The output of this research is an intelligent combined system of ANN and ANFIS. The obtained values well show the ability of this model to estimate energy consumption in the mentioned buildings with high accuracy.

The adaptive fuzzy neural inference system (ANFIS) is an efficient estimation model not only among fuzzy neural systems but also among other types of machine learning techniques. Despite its acceptance among researchers, ANFIS cited limitations such as inefficiencies in large data and data problems, cost of computation, processing time and optimization, and error training. The ANFIS structural design is a complex optimization problem that can be improved using meta-heuristic algorithms. In this study, to optimize and reduce errors, a new meta-heuristic algorithm inspired by nomadic migration was designed and used to design an adaptive fuzzy neural system called the Qashqai nomadic meta-heuristic algorithm. The results of the hypothesis test showed that the Qashqai optimization algorithm is not defeated by the genetic algorithm and particle swarm and works well in terms of convergence to the optimal answer. In this hybrid algorithm, random data set are first generated and then trained by designing a basic fuzzy neural system. Subsequently, the parameters of the basic fuzzy system were adjusted according to the modeling error using the meta-heuristic optimization algorithm of Qashqai nomads. The fuzzy nervous system with the best values was obtained as the final result.The main achievements of the study are:• Improving ANFIS accuracy using a novel meta-heuristic algorithm.• Fix and remove some problems and Limitations in the Anfis model, such as inefficiencies in large data, cost of computation, Answer accuracy, and reduce errors.• Comparing the proposed ANFIS+QA with some recent related work such as ANFIS+QA and ANFIS+Pso.