LoadCNN: A Low Training Cost Deep Learning Model for Day-Ahead Individual Residential Load Forecasting
This addresses the energy and environmental burden of deep learning training for smart grid applications, though it is incremental as it optimizes an existing method for a known bottleneck.
The paper tackles the problem of high training costs in deep learning for day-ahead individual residential load forecasting by proposing LoadCNN, a low-cost convolutional neural network model. The result shows that LoadCNN reduces training time to about 1/54, energy consumption and CO2 emissions to about 1/45 compared to state-of-the-art models while maintaining equal prediction accuracy.
Accurate day-ahead individual residential load forecasting is of great importance to various applications of smart grid on day-ahead market. Deep learning, as a powerful machine learning technology, has shown great advantages and promising application in load forecasting tasks. However, deep learning is a computationally-hungry method, and requires high costs (e.g., time, energy and CO2 emission) to train a deep learning model, which aggravates the energy crisis and incurs a substantial burden to the environment. As a consequence, the deep learning methods are difficult to be popularized and applied in the real smart grid environment. In this paper, we propose a low training cost model based on convolutional neural network, namely LoadCNN, for next-day load forecasting of individual resident with reduced training cost. The experiments show that the training time of LoadCNN is only approximately 1/54 of the one of other state-of-the-art models, and energy consumption and CO2 emissions are only approximate 1/45 of those of other state-of-the-art models based on the same indicators. Meanwhile, the prediction accuracy of our model is equal to that of current state-of-the-art models, making LoadCNN the first load forecasting model simultaneously achieving high prediction accuracy and low training costs. LoadCNN is an efficient green model that is able to be quickly, cost-effectively and environmentally-friendly deployed in a realistic smart grid environment.