دانلود مقاله ISI انگلیسی شماره 157377
ترجمه فارسی عنوان مقاله

یک مدل پیش بینی بار کوتاه مدت بار الکتریکی هوشمند برای شبکه های قدرت هوشمند

عنوان انگلیسی
An intelligent hybrid short-term load forecasting model for smart power grids
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
157377 2017 32 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Sustainable Cities and Society, Volume 31, May 2017, Pages 264-275

پیش نمایش مقاله
پیش نمایش مقاله  یک مدل پیش بینی بار کوتاه مدت بار الکتریکی هوشمند برای شبکه های قدرت هوشمند

چکیده انگلیسی

An accurate load forecasting is always particularly important for optimal planning and energy management in smart buildings and power systems. Millions of dollars can be saved annually by increasing a small degree of improvement in prediction accuracy. However, forecasting load demand accurately is a challenging task due to multiple factors such as meteorological and exogenous variables. This paper develops a novel load forecasting model, which is based on a feed-forward artificial neural network (ANN), to predict hourly load demand for various seasons of a year. In this model, a global best particle swarm optimization (GPSO) algorithm is applied as a new training technique to enhance the performance of ANN prediction. The fitness function is defined and a weight bias encoding/decoding scheme is presented to improve network training. Influential meteorological and exogenous variables along with correlated lagged load data are also empolyed as inputs in the presented model. The data of an ISO New England grid are used to validate the performance of the developed model. The results demonstrate that the proposed forecasting model can provide significanly better forecast accuracy, training performances and convergence characteristics than contemporary techniques found in the literature.