Abstract
Paper aims This study analyzed the feasibility of the BiGRU-CNN artificial neural network as a forecasting tool for short-term electric load. This forecasting model can serve as a support tool related to decision-making by companies in the energy sector. Originality Despite a large amount of scientific research in this area, the literature still searches for more assertive forecasting models regarding short-term electric load. Thus, the BiGRU-CNN model, based on layers of BiGRU and CNN architecture networks was tested. This model was already proposed and used for other similar tasks, however, it has not been used on load forecasting. Research method The code was programmed in Python using the keras package. The forecasts of all networks were carried out 10 times until an acceptable statistical sample was reached so that future electric load values are as close as possible to reality. Main findings The best forecasting model was the proposed BiGRU-CNN network when compared to classical and some hybrid networks. Implications for theory and practice This methodology can be applied to short-term electric load forecasting problems. There is evidence that the combination of different layers of neural networks can provide more efficient forecasting results than classical networks with only one architecture.
Highlights
Economic development around the world depends almost exclusively on the availability of electricity in industries, as most of them use it to carry out their vital productive activities (Soliman & Al-Kandari, 2010)
Given the possibility of using future energy demand values as a tool to support decision-making, this study aims to improve the short-term electricity demand forecast of a company in the electricity sector with the following proposed model based on different layers of artificial neural networks named bidirectional gated recurrent unit (BiGRU)-convolutional neural network (CNN)
The analysis of the tables with simulations involving 77.5 and 66.3% of the original historical series shows that the same relationship between the RNN and BiGRU-CNN networks is not maintained, because in the first one the results of MLP were closer to the BiGRU-CNN network and in the second one the performance of the CNN network was the least different
Summary
Economic development around the world depends almost exclusively on the availability of electricity in industries, as most of them use it to carry out their vital productive activities (Soliman & Al-Kandari, 2010). There are four types of electric load forecast horizons to achieve different planning objectives and to assist in the monitoring of critical conditions in the electrical system. According to Setiawan et al (2009), these forecast horizons can be classified as very short term, short term, medium term, and long term. Very short-term energy demand forecasts provide future values between one minute and one hour to determine the best strategy for the use of resources during energy generation (Charytoniuk & Chen, 2000). Short-term forecasts are carried out between one hour and one week to assist in operational planning, as electric load is defined one day before its production around the world (Chapagain et al, 2020).
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