A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation

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Abstract

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger–Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation.

Section snippets

Significance

This is the first study that presents a hybrid simulation-adaptive network fuzzy inference system (ANFIS) for improvement of electricity consumption estimation. The unique features of the proposed algorithm are two fold. First, ANFIS is ideal for complex and uncertain data because it is composed of both ANN and fuzzy systems. Second Monte Carlo simulation is used to generate input variables whereas the conventional methods use deterministic data. The superiority of the proposed algorithm is

The hybrid algorithm

The ANFIS model with preprocessed data (ANFISW) and ANFIS model without preprocessed data (ANFISWO) are considered to determine the impact of preprocessing on ANFIS. Moreover, the raw data is simulated by computer simulation to identify its probability distribution and the mean of probability distribution is then used as input data for ANFIS. This is of course repeated for each month. The advantage of simulated-based is to foresee if the stochastic nature of data has any impact on future demand

The case study

The proposed algorithm is applied to 130 set of data which are the monthly consumption in Iran from April 1994 to February 2005. Detailed information can be obtained from “Electric Power Industry in Iran” (including transmission and distribution) published by the TAVANIR management organization (1992–2005). As simulated data is important for us, the related process discussed at first.

Comparison with other intelligent methods

With the aid of ANFISW model, electricity for the next 12 month is forecasted (Fig. 11). Table 7 shows the MAPE estimation for genetic algorithm (GA), artificial neural network (ANN) versus the proposed algorithm (Azadeh et al., 2007, Azadeh et al., 2007). Examination of this table shows that the proposed algorithm provides good estimation with respect to GA and ANN.

Conclusion

This paper presented a hybrid adaptive network fuzzy inference system (ANFIS), computer simulation and time series techniques to estimate and predict energy consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation was developed to generate random variables for monthly

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