TY - JOUR ID - 772 TI - Electrical Load Manageability Factor Analyses by Artificial Neural Network Training JO - Journal of Operation and Automation in Power Engineering JA - JOAPE LA - en SN - 2322-4576 AU - Eskandari, N. AU - Jalilzadeh, S. AD - Department of Electrical Engineering, University of Zanjan, Zanjan, Iran Y1 - 2019 PY - 2019 VL - 7 IS - 2 SP - 187 EP - 195 KW - Soft Load behavior KW - load side management KW - load sensitivity KW - manageability factor KW - neural network DO - 10.22098/joape.2019.5398.1405 N2 - 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 UR - https://joape.uma.ac.ir/article_772.html L1 - https://joape.uma.ac.ir/article_772_71d20590e185c088b4d7e0259ef50201.pdf ER -