کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11015684 | 1782157 | 2018 | 57 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders
ترجمه فارسی عنوان
سری زمانی مصرف انرژی پیش بینی شده با استفاده از تکنیک های یادگیری ماشین بر اساس الگوهای استفاده از خانواده های مسکونی
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کلمات کلیدی
مصرف انرژی، ساختمان های مسکونی، پیش بینی سری زمانی، داده کاوی، هوش مصنوعی، فراگیری ماشین،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی (عمومی)
چکیده انگلیسی
Energy consumption in buildings is increasing because of social development and urbanization. Forecasting the energy consumption in buildings is essential for improving energy efficiency and sustainable development, and thereby reducing energy costs and environmental impact. This investigation presents a comprehensive review of machine learning (ML) techniques for forecasting energy consumption time series using actual data. Real-time data were collected from a smart grid that was installed in an experimental building and used to evaluate the efficacy and effectiveness of statistical and ML techniques. Well-known artificial intelligence techniques were used to analyze energy consumption in single and ensemble scenarios. An in-depth review and analysis of the 'hybrid model' that combines forecasting and optimization techniques is presented. The comprehensive comparison demonstrates that the hybrid model is more accurate than the single and ensemble models. Both the accuracy of prediction and the suitability for use of these models are considered to support users in planning energy management.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 165, Part B, 15 December 2018, Pages 709-726
Journal: Energy - Volume 165, Part B, 15 December 2018, Pages 709-726
نویسندگان
Jui-Sheng Chou, Duc-Son Tran,