Energy consumption prediction based on FB prophet -GRU method

Abstract

In modern commercial medium industries, electric energy consumption prediction plays a significant role in including the energy expenditure in the process of making the annual financial budget of the company. In this project, we predicted electric energy consumption using a model which combines FbProphet and GRU that we developed. We collected raw data from Igara Growers Tea Factory (IGTF) and refined using the MinMax scaler for effective training. We then developed a hybrid model with the integration of Fbprophet and a five-layer gated recurrent unit (GRU) and trained it by exploiting the transfer learning technique. The evaluation techniques used in the trained model were; the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. From the experimental results, we found that, in comparison with other existing models, the hybrid model we developed provided more accurate results with relatively lower errors which were: (RMSE = 0.0103), (MSE = 0.0001) and (MAE = 0.0076). The use of the model developed in this project will guide the industry in the proper allocation of resources by helping to estimate the correct amount to be spent on power in the stipulated duration of time in the future up to one year period. Further work on the prediction of Uganda’s future energy tariffs basing on the available past quarterly tariff data as determined by the energy regulatory bodies is necessary in order to evade the task of predicting future expenditure on electrical energy by the industry basing on the current tariff.