Electrical Load Manageability Factor analyses by Artificial Neural Network Training
نویسندگان
چکیده مقاله:
On typical medium voltage feeder, Load side management means power energy consumption controlling at connected loads. Each load has various amount of reaction to essential parameters variation that collection of these reactions is mentioned feeder behavior to each parameter variation. Temperature, humidity, and energy pricing variation or major event happening and power utility announcing to the customers are essential parameters that are considered at recent researches. Depends on amount of improvement that each changeable parameters effect on feeder load consumption, financial assets could be managed correctly to gain proper load side management. Collecting feeder loads behavior to all mentioned parameters will gain Load Manageability Factor (LMF) that helps power utilities to manage load side consumption. Calculating this factor needs to find out each types of load with unique inherent features behavior to each parameters variation. This paper and future works will help us to catch mentioned LMF. In this paper analysis of typical commercial feeder behavior due to temperature and humidity variation with training artificial neural network will be done. Load behavior due to other essential parameters variations like energy pricing variation, major event happening, and power utility announcing to the customers, and etc will study in future works
منابع مشابه
Residential Load Manageability Factor Analyses by Load Sensitivity Affected by Temperature
Load side management is the basic and significant principle to keeping the balance between generation side and consumption side of electrical power energy. Load side management on typical medium voltage feeder is the power energy consumption control of connected loads with variation of essential parameters that loads do reaction to their variation. Knowing amount of load's reaction to each para...
متن کاملElectrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network
Today, one of most important concerns in electrical power markets and distribution network is supplying the customer demands. In order to manage the market it is necessary to forecast the usage of electrical power in distribution network. The pattern of electrical power usage depends on many different parameters such as the week days, seasons, weather condition and etc. Today, researchers by us...
متن کاملArtificial Neural Network and ANFIS Based Short Term Load Forecasting in Real Time Electrical Load Environment
An efficient and accurate electrical power Short Term Load forecasting plays a vital role for economic operational planning of both the electricity markets as well as regulated power systems. Till date many techniques and approaches have been presented for STLF in the literature. However there is still an essential need to develop more efficient and accurate load forecast model. This paper uses...
متن کاملAnalysis of Electrical Load Forecasting by Using Matlab Tool Box through Artificial Neural Network
Load forecasting is a central integral process in the planning and operation of electric utilities. Load forecasting has become in recent years one of the major areas of research in electrical engineering. The main problem for the planning is the determination of load demand in the future. Because electrical energy cannot be stored appropriately, correct load forecasting is very essential for t...
متن کاملShort-term Electrical Load Forecasting for an Institutional/Industrial Power System Using an Artificial Neural Network
متن کامل
Detecting Depression in Elderly People by Using Artificial Neural Network
Introduction: The possibility of depression is common in the elderly. Novel technologies allow us to monitor people related to depression. Hence, a model was provided to detect depression in elderly based on artificial neural network (ANN). Methods: The present study is an applied descriptive-survey research. Forty elderly people were randomly selected from the Elderly Care Center in Gonbad Ka...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 7 شماره 2
صفحات 187- 195
تاریخ انتشار 2019-10-01
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023