A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks
المؤلفون المشاركون
Zhong, Qianwen
Peng, Lele
Zheng, Shubin
Chai, Xiaodong
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-13
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models.
However, it has long running time and relatively strong dependence on time and weather factors at a residential level.
To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure.
From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors.
To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model.
The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy.
Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time.
This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zheng, Shubin& Zhong, Qianwen& Peng, Lele& Chai, Xiaodong. 2018. A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1207408
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zheng, Shubin…[et al.]. A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks. Mathematical Problems in Engineering No. 2018 (2018), pp.1-16.
https://search.emarefa.net/detail/BIM-1207408
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zheng, Shubin& Zhong, Qianwen& Peng, Lele& Chai, Xiaodong. A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-16.
https://search.emarefa.net/detail/BIM-1207408
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1207408
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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