Energy Economics
Mojtaba Pourghorban; Siab Mamipour
Abstract
The restructuring of Iranian electricity industry allowed electricity price to be determined through market forces in 2005. The main purpose of this paper is to present a method for modeling and forecasting the electricity prices based on complex features such as instability, nonlinear conditions, and ...
Read More
The restructuring of Iranian electricity industry allowed electricity price to be determined through market forces in 2005. The main purpose of this paper is to present a method for modeling and forecasting the electricity prices based on complex features such as instability, nonlinear conditions, and high fluctuations in Iran during the spring 2013 and winter 2018. For this purpose, time-series data of the daily average electricity price was decomposed into one approximation series (low frequency) and four details series (high frequency) utilizing the wavelet transform technique. The approximation and details series are estimated and predicted by ARIMA and GARCH models, respectively. Then, the electricity price is predicted by reconstructing and composing the forecasted values of different frequencies as a proposed method (Wavelet-ARMA-GARCH). The results demonstrated that the proposed method has higher predictive power and can forecast volatility of electricity prices more accurately by taking into consideration different domains of the time-frequency; although, more errors are produced if the wavelet transform process is not used. The mean absolute percentage error values of the proposed method during spring 2017 to winter 2018 are significantly less than that of the alternative method, and the proposed method can better and more accurately capture the complex features of electricity prices.
Afsaneh Kazemi Mehrabadi; Vahid Taghinezhad Omran; Mohammad Valipour Khatir; Saeed Rasekhi
Abstract
Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features has made significant progress in recent years. The current study compared the accuracy ...
Read More
Forecasting industrial production is essential for efficient planning by managers. Although there are many statistical and mathematical methods for prediction, the use of intelligent algorithms with desirable features has made significant progress in recent years. The current study compared the accuracy of the Artificial Neural Networks (ANN) and Adaptive Nero-Fuzzy Inference System (ANFIS) approaches to assess the current state and predict the future state of industrial production. The seasonal dataset comprised the labor force, capital stock, human capital, trade openness, liquidity and credit financing to the industrial sector as input variables and value added of industrial production as the output variable for the period of 1988 to 2018. The dataset was used to forecast industrial production for Seasons of the year 2019 and 2020. The results showed that, while both are appropriate tools for forecasting industrial production, ANFIS had a lower the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) than ANN. The findings of the research indicate that ANFIS is more effective in forecasting industrial production, which can help policymakers in planning and creating an effective strategy for the future.
Abstract
Forecasting energy price and consumption is essential in making effective managerial decisions and plans. While there are many sophisticated mathematical methods developed so far to forecast, some nature-based intelligent algorithms with desired characteristics have been developed recently. The main ...
Read More
Forecasting energy price and consumption is essential in making effective managerial decisions and plans. While there are many sophisticated mathematical methods developed so far to forecast, some nature-based intelligent algorithms with desired characteristics have been developed recently. The main objective of this research is short term forecasting of energy price and consumption in Iranian industrial sector using artificial intelligence including an Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Artificial Neural Networks (ANN). The dataset contains monthly price and consumption of gas oil, petrol, and liquid petroleum gas in the period between March 1996 and March 2010. Based on dataset, energy price and consumption for 2011 and 2012 are forecasted. The results obtained utilizing the two methods show that while both are appropriate tools to forecast price and consumption, most of the time ANFIS has lower error than ANN in terms of the mean squared error criterion
