کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11007383 | 1521388 | 2018 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Effective long short-term memory with differential evolution algorithm for electricity price prediction
ترجمه فارسی عنوان
حافظه بلند مدت کوتاه مدت با الگوریتم تکاملی دیفرانسیل برای پیش بینی قیمت برق
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کلمات کلیدی
حافظه طولانی مدت، الگوریتم تکامل دیفرانسیل، پیش بینی قیمت برق،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
Electric power, as an efficient and clean energy, has considerable importance in industries and human lives. Electricity price is becoming increasingly crucial for balancing electricity generation and consumption. In this study, long short-term memory (LSTM) with the differential evolution (DE) algorithm, denoted as DE-LSTM, is used for electricity price prediction. Several recent studies have adopted LSTM with considerable success in certain applications, such as text recognition and speech recognition. However, problems in the application of LSTM to solving nonlinear regression and time series problems have been encountered. DE, a novel evolutionary algorithm that effectively obtains optimal solutions, is designed to identify suitable hyperparameters for LSTM. Experiments are conducted to verify the performance of the DE-LSTM model under the electricity prices in New South Wales, Germany/Austria, and France. Results indicate that the proposed DE-LSTM model outperforms existing forecasting models in terms of forecasting accuracies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 162, 1 November 2018, Pages 1301-1314
Journal: Energy - Volume 162, 1 November 2018, Pages 1301-1314
نویسندگان
Lu Peng, Shan Liu, Rui Liu, Lin Wang,