Abstract
The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy.
Highlights
Nowadays, power consumption is gradually increasing due to rising introduction of smart factories, electric cars, and embedded systems by adapting the concept of automatization, unmanned plant, and artificial intelligence in the industry
One of the most important things to operate power systems is that the supply and the demand of electric power should fit the balance within a fixed range of electricity reservation ratio
In order to use temperature as a variable based on the prediction date, we obtained the highest temperatures of day in Seoul, Gwangju, and Busan from 1 January 2013 to 31 December 2017 one temperatures of day in Seoul, Gwangju, and Busan from 1 January 2013 to 31 December 2017 one year ago based on the amount of electric power transaction (EPT) from 1 January 2014 to 31 December 2018
Summary
Power consumption is gradually increasing due to rising introduction of smart factories, electric cars, and embedded systems by adapting the concept of automatization, unmanned plant, and artificial intelligence in the industry. The KPX is known as a joint-stock company that maximizes profit on its business It needs stable electric power trading and optimized operation planning. If its prediction of the amount of EPT such as demand and supply of the electric power is not accurate, two problems can occur. If predicted electric demand is less than electric supply amount, the lack of electricity reserve can happen, causing a blackout These two items can increase instability of electric power systems. In South Korea, for example, the KPX failed to predict electric power demand or consumption on 15 September 2011. As a result, they experienced a power outage.
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