The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Nowadays, many researches are made to estimate some of socio-economic variables in which methods such as regression, time series (ARIMA, AR and etc.), Artificial Neural Networks (ANN) and so on are used. In this paper integrated System Approach and ANN are applied for estimating affects of subsidy on electricity consumption and social welfare. Actual electricity price is estimated by ANN, which has...
One of the basic requirements for power systems is accurate short-term load forecasting (STLF). In this study, the application of artificial neural networks is explored for designing of short-term load forecasting systems for electricity market of Iran. In this paper, two seasonal artificial neural networks (ANNs) are designed and compared; so that model 2 (hourly load forecasting model) is partitioning...
This paper compares two expert models in daily stream flow forecasting. The adaptive-neuro fuzzy inference system (ANFIS) model, artificial neural networks with radial base function (RBF) are used to forecast daily river flow in northwest of Iran and the results of these models are compared with Observed daily values. Daily river flow data in Mahabad-Dam station on Mahabad river in northwest of Iran...
Accurate demand forecasting is one of the most crucial issues in inventory management of spare parts in process industries. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of these methods may perform...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.