• سیاستگذاریهای اقتصادی و مالی در حوزههای فوقالذکر در سطوح ملی، منطقهای و جهانی
reza taleblou; parisa Mohajeri
Abstract
This study investigates the application of recurrent neural network (RNN) models—specifically RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)—in predicting the stock indices of the Iranian energy industry. Using daily time series data from May 1, 2020, to May 1, 2024, the ...
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This study investigates the application of recurrent neural network (RNN) models—specifically RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)—in predicting the stock indices of the Iranian energy industry. Using daily time series data from May 1, 2020, to May 1, 2024, the dataset was divided into a training period (80%) and a testing period (20%). In the first step, the optimal architectures of each model (estimating hyper-parameters) were determined for prediction horizons of 1, 2, 5 (one week), and 20 trading days (one month). Subsequently, prediction errors of the three machine learning models were compared with the linear econometric model (ARIMA) across various forecast horizons. The findings in two areas of cross validations of machine learning models as well as predication error reveal the following insights: First, as the forecast horizon increases, the batch size of optimal prediction decreases for all three machine learning models, and the larger the input training sample size leads to the smaller batch size. Second, in short-term forecast horizons (1, 2, and 5 trading days), machine learning models—particularly LSTM—demonstrate lower prediction errors than ARIMA, while in the 20-trading-day (1-month) forecast horizon, ARIMA's predictive accuracy approaches to the nonlinear machine learning models. Third, forecast accuracy decreases as the horizon lengthens, with accuracy dropping from approximately 98.5% (for a 1-day horizon) to 92.5% (for a 20-day horizon). Finally, selecting the appropriate forecasting method for the stock market indices of energy industries depends on the forecast horizon and data characteristics.
Omid Motamedi sede; Bakhtiar Ostadi; ali hussein zadeh kashan; mohamad reza amin naseri
Abstract
The deregulation in power market is lead to competition among market participant to increase efficiency. In electricity market generation is the best candidate for iterance in competition to improve productivity and efficiency in resource allocation and offer lowest price by highest quality will be yielded. ...
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The deregulation in power market is lead to competition among market participant to increase efficiency. In electricity market generation is the best candidate for iterance in competition to improve productivity and efficiency in resource allocation and offer lowest price by highest quality will be yielded. In the pool-based electricity market, every Genco submits a bidding price in ten step offer to the Independent System Operator (ISO) for every hour of the next day. One of the most important parameters affecting the profitability of Genco’s and their bidding pattern is the cost of energy generation. Therefore, in this paper, taking into account the costs of fuel consumption, depreciation, maintenance, operation and utilization and other costs as factors affecting the production cost. In this article artificial intelligence algorithms applied to calculate the energy generation cost function at different levels of production (base load, partial load, and full load), broken down by fuel type. According to the results, the average cost per kilowatt-hour of energy with gas fuel is 398 Rials and diesel fuel is 500 Rials
