A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart
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ORIGINAL PAPER
A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid Ömer Faruk Ertugrul ˘ 1 · Hazret Tekin1 · Ramazan Tekin2 Received: 31 January 2020 / Accepted: 25 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The short- and long-term forecasting of the grid electrical energy of a province or region is important for the management of the conventional electricity transmission and distribution network. Nowadays, the amount of electrical energy, which each residential building has taken from the grid, has gained importance within the scope of smart grids. Residential buildings that take electricity from the smart grid can generate electricity with alternative energy sources such as solar energy. Besides generating its energy these buildings can also take electricity from the electricity grid. In other words, they can use both the energy that they generate and the energy that they receive from the electricity grid. This bi-directional energy flow increases system complexity, and the grid system become more dynamic. Therefore, estimating the electrical load of such a system is also more difficult than the conventional grid system. In this study, the recurrent linear regression method (R-LR), which is based on the linear regression, was proposed. In order to test and validate the proposed approach, the Sundance dataset, which is shared in the U mass trace repository according to smart project was used. To confirm the success of the proposed method, the linear regression (LR), and the extreme learning machine (ELM) methods were used in each of the 59 different datasets. Obtained results, which were applied to 59 different residential buildings smart meter datasets, showed that lower root means square error and corrected symmetric mean absolute percentage error, which was proposed in this paper in order to eliminate zero dividing errors, values were achieved by R-LR compared to LR and ELM. It was found that the proposed R-LR provides better results in modeling dynamic systems and gives good results in forecasting analysis with a time-series dataset. Keywords Smart grid · Recurrent linear regression · Linear regression · Sundance dataset · Short-term grid load estimation in buildings
1 Introduction The conventional electrical distribution network has developed according to the improvements in technology. These developments have provided more efficient use of electrical energy. In addition to this fact, the aim of using renewable energy resources because of reducing the use of fossil resources in the energy production sector is another locomotive of this development. In line with these objectives and as a result of advances in the field of electronics and communi-
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Ömer Faruk Ertu˘grul [email protected]
1
Department of Electrical-Electronics Engineering, Batman University, 72060 Batman, Turkey
2
Department of Computer Engineering, Batman University, 72060 Batman, Turkey
cation, radical changes
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