Very short term load forecasting of a distribution system with high PV penetration

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].


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.


2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.


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