{'fraction_non_alphanumeric': 0.025735294117647058, 'fraction_numerical': 0.006740196078431373, 'mean_word_length': 4.9381818181818184, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Leveraging Spatial Information in Smart Grids using STGCN for Short-Term Load Forecasting

The problem of predicting the behaviour of energy consumers (loads) in the next few intervals — Short-Term Load Forecasting (STLF) is critical to the success of several grid operations. Prediction at lower aggregation levels is difficult due to the high volatility of the data. Smart grid operations, and in turn any data generated as a result of them, exhibit high spatial correlations imposed due to the topology of the power distribution network as well as other latent factors such as similarity in neighborhood, socio-economic status, etc. While temporal information is usually leveraged in neural network structures like Recurrent or Convolutional Layers, the use of spatial information in load forecasting has not been explored. In this paper, we develop a Spatial-Temporal Graph Convolutional Network (STGCN) model for the problem of Short-Term Load Forecasting in Smart Grids. STGCNs specialize in capturing both spatial and temporal correlations in the data to obtain more accurate predictions. We also show that our model, by capturing both spatial and temporal correlations, is more robust to missing data than state-of-the-art prediction models. We perform detailed evaluation on a dataset based in Iowa, US with real power at a low aggregation level (5 ∼ 10 customers per datapoint) and show that our model predicts 3 hours ahead real load consumption with a Mean Absolute Error of 7.54% less than the best performing baseline model, and as much as 38.72% less in Root Mean Squared Error (RMSE) if the data has missing entries.