دانلود رایگان مقاله ترکیب دانش دامنه به بهینه سازی سیستم های انرژی
|عنوان فارسی:||ترکیب دانش دامنه به بهینه سازی سیستم های انرژی|
|عنوان انگلیسی:||Incorporating domain knowledge into the optimization of energy systems|
|تعداد صفحات مقاله انگلیسی : 11||تعداد صفحات ترجمه فارسی : ترجمه نشده|
|سال انتشار : 2016||نشریه : الزویر - Elsevier|
|فرمت مقاله انگلیسی : PDF||کد محصول : E2183|
|محتوای فایل : PDF||حجم فایل : 1 Mb|
|رشته های مرتبط با این مقاله: مهندسی کامپیوتر|
|گرایش های مرتبط با این مقاله: داده کاوی|
|مجله: محاسبات کاربردی نرم - Applied Soft Computing|
|دانشگاه: ترنتو، ایتالیا|
|کلمات کلیدی: دانش دامنه، بهینه سازی سیستم های انرژی، مقداردهی اولیه، الگوریتم تکاملی چند هدفه|
Energy plays a key factor in the advancement of humanity. As energy demands are mostly met by fossil fuels, the world-wide consciousness grows about their negative impact on the environment. Therefore, it becomes necessary to design sustainable energy systems by introducing renewable energies. Because of the intermittent availability of different renewable resources, the designing of a sustainable energy system should find an optimal mix of different resources. However, the optimization of this combination has to deal with a number of possibly contradictory objectives. Multi-objective evolutionary algorithms (MOEA) are widely used to solve this kind of problems. As optimizing an energy system by using a MOEA is computationally costly, it is necessary to solve the problem efficiently. For this purpose, we propose the incorporation of domain knowledge related to energy systems into different phases (i.e., initialization and mutation) of a MOEA run. The proposed approaches are implemented for two widely used MOEAs and evaluated on the Danish Aalborg test problem. The experimental results show that each approach individually achieves significant improvements of the energy systems, which is expressed in better trade-off sets. Moreover, a state-of-the-art stopping criterion is adapted to detect the convergence in order to save computational resources. Finally, all proposed techniques are merged within two MOEAs with the result that our combined approaches yield significantly better results in less time than generic approaches.
Current and future energy systems will include more and more renewable energy sources. To accurately plan such systems, complex and computationally costly simulations are typically used to assess configurations according to different objectives,for example, based on their cost and their emissions. General purpose multi-objective evolutionary algorithms are often used to solve such problems, however, the simulation cost result in time-consuming optimizations. In this article, we present and combine different techniques to improve both solution quality and speed of an optimization. First and foremost, we incorporate basic domain knowledge about energy systems into different operators of such algorithms in order to increase the solution quality. In addition, we also adapt a recently-developed stopping criterion to save simulations.