Short-Term Load Forecasting in the Distribution System of the Electric Company of Ambato (EEASA) Based on Big Data Criteria

Author(s):  
Alexis Guaman ◽  
Juan Ramirez ◽  
Bryan Mayorga ◽  
Fausto Aviles ◽  
Carlos Gallardo
2021 ◽  
Vol 2143 (1) ◽  
pp. 012040
Author(s):  
Yang Donghui

Abstract Short-term load forecasting of power system is an important task of power distribution system. Accurate short-term load forecasting provides the best configuration for grid power generation and distribution, maximizing energy saving and ensuring stable operation. This paper aims to study the design of short-term load forecasting system of power system based on big data. On the basis of analyzing power system load forecasting algorithms, classification of load forecasting, characteristics of load forecasting and system design principles, each module of the system is designed in detail, and finally tested the performance of the system. The test results show that the system has no adverse reactions in the use of a large number of users and repeated operation for a long time. In the case of large throughput, the system has a satisfactory response time and relatively reliable system stability.


2017 ◽  
Vol 106 ◽  
pp. 142-148 ◽  
Author(s):  
Saeed Sepasi ◽  
Ehsan Reihani ◽  
Abdul M. Howlader ◽  
Leon R. Roose ◽  
Marc M. Matsuura

2018 ◽  
Vol 13 (6) ◽  
pp. 938-955
Author(s):  
Violeta Eugenia Chis ◽  
Constantin Barbulescu ◽  
Stefan Kilyeni ◽  
Simona Dzitac

A software tool developed in Matlab for short-term load forecasting (STLF) is presented. Different forecasting methods such as artificial neural networks, multiple linear regression, curve fitting have been integrated into a stand-alone application with a graphical user interface. Real power consumption data have been used. They have been provided by the branches of the distribution system operator from the Southern-Western part of the Romanian Power System. This paper is an extended variant of [4].


2014 ◽  
Vol 687-691 ◽  
pp. 1186-1192 ◽  
Author(s):  
Xin Zhang ◽  
Ming Cheng ◽  
Yang Liu ◽  
Dong Hua Li ◽  
Rui Min Wu

In recent years, wide installation of smart meters and implementation of Smart Meter Management System (SMMS) provides data foundation for short-term load forecasting. In this paper, a new load forecasting approach is proposed based on big data technologies using smart meter data. The new approach analyzes the characteristics of numerous electricity users, which helps system operators identify influencing factors. Big data architecture can handle large amount of data and computation efforts. Compared with the traditional system load forecasting methods, this new approach produces better prediction accuracy.


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