کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6875022 1441467 2018 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders
ترجمه فارسی عنوان
یک مدل عمیق کارآمد برای پیش بینی میزان بار الکتریکی روز قبل با استفاده از خودکار رمزگذاری انحصاری انبوه
کلمات کلیدی
یادگیری عمیق، چند منظوره خودکار رمزگذاری انحصاری انباشته شده، استخراج ویژگی، رگرسیون بردار پشتیبانی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی
In real word it is quite meaningful to forecast the day-ahead electricity load for an area, which is beneficial to reduction of electricity waste and rational arrangement of electric generator units. The deployment of various sensors strongly pushes this forecasting research into a “big data” era for a huge amount of information has been accumulated. Meanwhile the prosperous development of deep learning (DL) theory provides powerful tools to handle massive data and often outperforms conventional machine learning methods in many traditional fields. Inspired by these, we propose a deep learning based model which firstly refines features by stacked denoising auto-encoders (SDAs) from history electricity load data and related temperature parameters, subsequently trains a support vector regression (SVR) model to forecast the day-ahead total electricity load. The most significant contribution of this heterogeneous deep model is that the abstract features extracted by SADs from original electricity load data are proven to describe and forecast the load tendency more accurately with lower errors. We evaluate this proposed model by comparing with plain SVR and artificial neural networks (ANNs) models, and the experimental results validate its performance improvements.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Parallel and Distributed Computing - Volume 117, July 2018, Pages 267-273
نویسندگان
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