A hybrid simulation-adaptive network based fuzzy inference system 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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2017, EnergyCitation Excerpt :Data mining approach is applied to extract the rules for constructing fuzzy system estimation in this study. In Ref. [19], a hybrid ANFIS and computer simulation is proposed to improve the accuracy of energy consumption forecasting. Despite the satisfactorily performance of ANN, SVR, ANFIS, and hybrid form of these methods for energy consumption forecasting, the main shortcoming still is black-box problem that they do not provide the knowledge of process for obtaining a solution.
Modelling a combined method based on ANFIS and neural network improved by DE algorithm: A case study for short-term electricity demand forecasting
2016, Applied Soft Computing JournalCitation Excerpt :For the last several decades, Artificial Intelligence (AI) methods have demonstrated the formidable ability in dealing with the seasonal and nonlinear load data. More and more new methodologies and techniques have emerged, such as the fuzzy logic system [9,10], the expert system [11,12], the grey prediction models [13,14] the Artificial Neural Networks (ANN) [15–17], the fuzzy inference system [18–20]. Among all these methods, artificial neural networks and fuzzy theories are well received.
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Member of Young Researcher Club of Azad University of Tafresh.