دانلود مقاله ISI انگلیسی شماره 52694
عنوان فارسی مقاله

مدل های یادگیری مصرف و تقاضای انرژی از طریق داده کاوی برای مهندسی معکوس

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52694 2015 6 صفحه PDF سفارش دهید 2560 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Learning Energy Consumption and Demand Models through Data Mining for Reverse Engineering ☆
منبع

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

Journal : Procedia Engineering, Volume 118, 2015, Pages 1319–1324

کلمات کلیدی
مهندسی معکوس - داده کاوی - مصرف انرژی - تحلیل آماری - ویژگی های عرضه و تقاضا
پیش نمایش مقاله
پیش نمایش مقاله مدل های یادگیری مصرف و تقاضای انرژی از طریق داده کاوی برای مهندسی معکوس

چکیده انگلیسی

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

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