Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction

Abstract

Energy prediction is an important process in energy management, especially regarding demand response. Energy predictions are often carried out for load forecasting or energy generation forecasting of renewable energy. This paper explains the implementation of multi-variables in the development of recurrence neural network models to predict load energy and generation energy. The proposed main model is a multi-variate bidirectional GRU combined with a periodic feature pattern. The proposed model will also be compared with the fundamental bidirectional models of the GRU and LSTM models. For load prediction, the variables used are all energy supply data and periodic features. Meanwhile, for photovoltaic generation energy predictions, additional weather data is used because energy generation is very dependent on solar radiation and ambient conditions. Load prediction data is built using daily and hourly energy prediction data. Meanwhile, solar energy prediction is constructed with data every minute. The results show that the proposed model obtains the best prediction results for all test data on a daily, hourly, or minute basis. The model also shows the fastest execution time performance compared to other models.