A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities
المؤلفون المشاركون
Rodriguez-Echeverria, Roberto
Preciado, Juan C.
Conejero, José M.
Prieto, Álvaro E.
Benitez, Rafael
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-06-03
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN).
The huge amount of data generated contain measurements such as flow or pressure.
Applying suitable algorithms to these data can warn about the possibility of leakage within the distribution network as soon as the data are gathered.
Currently, the algorithms that deal with this problem are the result of numerous short-term water demand forecasting (WDF) approaches.
However, in general, these WDF approaches share two shortcomings.
The first one is that they provide low-frequency predictions.
That is, most of them only provide predictions with 1-hour time steps, and only a few provide predictions with 15 min time steps.
The second one is that most of them require estimating the annual seasonality or taking into account not only data about water demand but also about other factors, such as weather data, that make their use more complicated.
To overcome these weaknesses, this work presents an approach to forecast the water demand based on pattern recognition and pattern-similarity techniques.
The approach has a twofold contribution.
Firstly, the predictions are provided with 1 min time steps within a time lead of 24 hours.
Secondly, the laborious estimation of annual seasonality or the addition of other factors, such as weather data, is not needed.
The paper also presents the promising results obtained after applying the approach for water demand forecasting to a real project for the detection and location of water leakages.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Preciado, Juan C.& Prieto, Álvaro E.& Benitez, Rafael& Rodriguez-Echeverria, Roberto& Conejero, José M.. 2019. A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities. Scientific Programming،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1210764
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Preciado, Juan C.…[et al.]. A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities. Scientific Programming No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1210764
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Preciado, Juan C.& Prieto, Álvaro E.& Benitez, Rafael& Rodriguez-Echeverria, Roberto& Conejero, José M.. A High-Frequency Data-Driven Machine Learning Approach for Demand Forecasting in Smart Cities. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1210764
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1210764
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