Towards Online Deep Learning-Based Energy Forecasting

Deep learning, as an increasingly powerful and popular data analysis tool, has the potential to improve smart grid operation. One critical issue is that the accuracy of deep learning relies heavily on the integrity of the training dataset, and the data collection process is time-consuming and complex, resulting in that the applying deep learning may not satisfy the needs of time-sensitive applications. Moreover, in the smart grid, predictions must be timely, and cannot wait for the initial dataset to be completely collected by the sensors. Also, the traditional centralized data analytics structure requires the entire dataset to be uploaded to the cloud datacenter for analysis, which incurs significant network resource and increases network congestion. To address these problems, in this paper we consider the allocation of deep learning at the network edge and directly in the Internet of Things (IoT) devices and design an online learning approach to enable small data subset training and continuous model updating to ensure accuracy requirements in time-sensitive environments. In our online learning approach, we implement the Just Another Network model, an optimized Long-Short Term Memory neural network model, to reduce the computation overhead for the deep learning training process. We evaluate our approach using real-world smart grid dataset. Our experimental results show that our online learning approach significantly reduces the training time while satisfying the accuracy requirements.