Forecasting Electricity Generated in A Rankine Power Station Running in Grid-connected Mode

Abstract

To ensure proactive and intelligent control of the electrical power generated in industrial units selfproducing energy, the construction of a data-driven model devoted to power prediction is an efficient solution that we have proven in this contribution. The remarkable added value of the paper was articulated around three main pillars: primarily, we have modelled a power station based on Rankine cycle that serves the energy needs of an industrial facility. Secondly, the connection of this plant to the public electric grid has been taken into consideration in modelling. Thirdly, we have used the model to avoid paying unwanted bills thanks to the forecasting of periods when there is an energy importation from grid. Before constructing the model, we have prepared a dataset composed of more than 25 million data points, reflecting the history of 3 years of archived data related to fifteen 15 features, in order to predict the target variable of this study. After finding that GRU and LSTM are among the most successful deep learning algorithms in energy prediction, we decided to use them in our particular application. Afterwards, a comparative study between them was first carried out on accuracy, where encouraging results were obtained on both sides, with respective scores of 98.73% and 99.36% during the training step, followed by 98.51% and 99.11% during the testing phase. Besides, the evaluation in terms of loss functions gave a convergence of the GRU model after 8 epochs, against 11 epochs for the LSTM algorithm. Therefore, we preferred to use LSTM in the definitive model because of its higher accuracy, providing that good computing power is used, so that the processing time could be acceptable by the endusers. Finally, we deployed the model to simulate a real case of forecasting the power drop one week in advance. In this case, we found that if the industrial unit had used the results of the model as a proactive decision support tool, it would have avoided importing 1456.4 MWh for 8 days from the national grid, which was equivalent to a loss of 119,094.8 USD.