scholarly journals Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods

Author(s):  
Andrei Marinescu ◽  
Colin Harris ◽  
Ivana Dusparic ◽  
Siobhan Clarke ◽  
Vinny Cahill
Author(s):  
Andrei Marinescu ◽  
Colin Harris ◽  
Ivana Dusparic ◽  
Vinny Cahill ◽  
Siobhan Clarke

2016 ◽  
Vol 8 (2) ◽  
pp. 411-447 ◽  
Author(s):  
Iman Ghalehkhondabi ◽  
Ehsan Ardjmand ◽  
Gary R. Weckman ◽  
William A. Young

2021 ◽  
pp. 111396
Author(s):  
Meritxell Gomez-Omella ◽  
Iker Esnaola-Gonzalez ◽  
Susana Ferreiro ◽  
Basilio Sierra

2020 ◽  
Vol 2 (1) ◽  
pp. 15-22
Author(s):  
Nurul Hudaningsih ◽  
Silvia Firda Utami ◽  
Wari Ammar Abdul Jabbar

Forecasting in the company is forecasting product sales to consumers. By knowing product sales can assist the company to provide materials to be produced and determine the production process itself. PT. Sunthi Sepuri is a pharmaceutical company. PT. Sunthi Sepuri often experiences marketing forecasting errors. This causes uncertainty in the amount of production so that it can cause employee productivity to decrease due to the increasing amount of production at any time. In this study demand forecasting will be held at PT. Sunthi Sepuri. This research apply the Single Moving Average and Single Exponential Smoothing methods, with the sample to be used is Aknil product, this product is a pain-relieving drug. Use the two methods to compare the most accurate forecasting methods and close to the actual value. The research methods start from gathering historical data, determining forecasting methods, forecasting calculations, determining the best method, and withdrawing conclusions. Based on the test results that the method that can be used to analyze data that has a small error rate is the Single Moving Average method. Forecasting results for July 2019 with the Single Exponential Smoothing method using ?: 0.8 are 408,488 caplets. As for July 2019, the Single Moving Average method is 466


Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3494
Author(s):  
Kuo Feng ◽  
Chunhua Liu ◽  
Zaixin Song

Multiple small-scale low-voltage distribution networks with distributed generators can be connected in a radial pattern to form a multi-bus medium voltage microgrid. Additionally, each bus has an independent operator that can manage its power supply and demand. Since the microgrid operates in the market-oriented mode, the bus operators aim to maximize their own benefits and expect to protect their privacy. Accordingly, in this paper, a distributed hour-ahead energy trading management is proposed. First, the benefit optimization problem of the microgrid is solved, which is decomposed into the local benefit optimization sub problems of buses. Then, the local sub problems can be solved by the negotiation of operators with their neighbors. Additionally, the reference demand before negotiation is forecasted by the neural network rather than given in advance. Furthermore, the power flow constraints are considered to guarantee the operational stability. Meanwhile, the power loss minimization is considered in the objective function. Finally, the demonstration and simulation cases are given to validate the effectiveness of the proposed hour-ahead energy trading management.


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