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Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer
المؤلفون المشاركون
Castelli, Mauro
Trujillo, Leonardo
Vanneschi, Leonardo
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-05-28
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
Energy consumption forecasting (ECF) is an important policy issue in today’s economies.
An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs.
The paper proposes a semantic based genetic programming framework to address the ECF problem.
In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data.
The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method.
The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter.
Experimental results confirm the suitability of the proposed method in predicting the energy consumption.
In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset.
More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Castelli, Mauro& Trujillo, Leonardo& Vanneschi, Leonardo. 2015. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1057791
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Castelli, Mauro…[et al.]. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1057791
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Castelli, Mauro& Trujillo, Leonardo& Vanneschi, Leonardo. Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1057791
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1057791
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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