Very short term load forecasting based on meteorological with modelling k-NN-feed forward neural network
المؤلفون المشاركون
Kartini, Unit Three
Ardianto, Dwi
Wardani, Laili
المصدر
العدد
المجلد 15، العدد 1 (31 مارس/آذار 2019)، ص ص. 1-16، 16ص.
الناشر
تاريخ النشر
2019-03-31
دولة النشر
الجزائر
عدد الصفحات
16
التخصصات الرئيسية
الموضوعات
الملخص EN
This paper proposes a novel methodology for very short term load forecasting of hourly.
The proposed methodology is based on meteorology data i.e.
temperature, humidity, especially for optimizing the operation of power generating electricity from thermal unit generation.
This modelling methodology is a combination of k-nearest neighbor (k-NN) method and feed forward- Neural Network (Feed-Forward-NN) method.
The k-NN-Feed-Forward NN model is designed to prediction load for 1 hour ahead based on meteorology data for the target Thermal Unit Generation which position adjacent by twelve hydro thermal unit generation.
The novelty of this model is taking into account the meteorology data.
A set of load measurement samples was available from the hydro thermal unit generation in Indonesia Region 4 which is used as test data.
The first model implements k-NN as a input data preprocessing technique prior to feed forward NN model.
The error statistical indicators of k-NN-Feed-Forward-NN method The mean absolute deviation error statistical indicators of k-NN model is 103.48 MW and MAPE is 18.8%.
On the other hand, the error statistical indicator for proposed model (Euclidean k-NNfeed forward-NN model) MAD is 19.37 MW and MAPE is 2.21%.
Note that the highest mean absolute deviation (MAD) was 75,11 MW and mean absolute percentage error (MAPE) was 10.38% during the twelve period.
The models forecasts are then compared to measured data and simulation results indicate that the k-NN-Feed Forward NN-based method presented in this research can calculate hourly load with satisfactory accuracy.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Kartini, Unit Three& Ardianto, Dwi& Wardani, Laili. 2019. Very short term load forecasting based on meteorological with modelling k-NN-feed forward neural network. Journal of Electrical Systems،Vol. 15, no. 1, pp.1-16.
https://search.emarefa.net/detail/BIM-861510
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Kartini, Unit Three…[et al.]. Very short term load forecasting based on meteorological with modelling k-NN-feed forward neural network. Journal of Electrical Systems Vol. 15, no. 1 (Mar. 2019), pp.1-16.
https://search.emarefa.net/detail/BIM-861510
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Kartini, Unit Three& Ardianto, Dwi& Wardani, Laili. Very short term load forecasting based on meteorological with modelling k-NN-feed forward neural network. Journal of Electrical Systems. 2019. Vol. 15, no. 1, pp.1-16.
https://search.emarefa.net/detail/BIM-861510
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 15-16
رقم السجل
BIM-861510
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