Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems
المؤلفون المشاركون
Acakpovi, Amevi
Ternor, Alfred Tettey
Asabere, Nana Yaw
Adjei, Patrick
Iddrisu, Abdul-Shakud
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-08
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS).
The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring.
A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana.
The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation.
Three scenarios were considered for the simulation exercise that was done with the MATLAB software.
Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3.
The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA).
Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values.
Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Acakpovi, Amevi& Ternor, Alfred Tettey& Asabere, Nana Yaw& Adjei, Patrick& Iddrisu, Abdul-Shakud. 2020. Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1195015
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Acakpovi, Amevi…[et al.]. Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems. Mathematical Problems in Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1195015
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Acakpovi, Amevi& Ternor, Alfred Tettey& Asabere, Nana Yaw& Adjei, Patrick& Iddrisu, Abdul-Shakud. Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1195015
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1195015
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