Hierarchical Multiobjective Distributed Deep Learning for Residential Short-Term Electric Load Forecasting

Abstract

Short-term load forecasting plays an essential role in appliance control in households and demand response at the neighborhood or community level. When load forecasting is simultaneously performed at different levels in a smart community, a central method that directly aggregates raw household data may incur a security issue. However, an individual method that builds separate models for each level requires additional data collection. We propose a distributed load forecasting framework that is secure, data efficient, and resource efficient. Our proposed method shares the latent variables, the reduced expression of the input data, of the household models without revealing the raw data. The latent variables are aggregated and fed to the neighborhood model, and during inference, input data collection can be abbreviated in the neighborhood. Because household models provide informative latent variables, the number of model parameters for the neighborhood model can be reduced. Our proposed method is evaluated using real electricity consumption data for six different models under 24 h and 1 h ahead forecasting. Our proposed method with the long short-term memory, multi-layer perceptron, and convolution neural network-based models presents the degradation in average mean absolute percentage error 3.68% and 5.16% at most for household and neighborhood forecasting, respectively, compared to the individual method. At the same time, it reduces the neighborhood’s model size by approximately 53%.

Keywords

• Demand forecasting
• distributed collaborative learning
• federated learning (FL)
• split learning (SL)
• multi-task learning
• recurrent neural networks (RNN)