Intraday time series load forecasting using Bayesian deep learning method—a new approach.

The accurate short-term load forecasting can pave the way for improving, planning and maintenance of electric power systems. In this work, a novel Bayesian deep learning framework has been developed on BiLSTM + dropout network for analyzing short-term load prediction. The proposed framework improves the prediction accuracy by learning the uncertainties as well as reducing the data overfitting. Furthermore, a hierarchical clustering technique is applied to group consumers of similar behaviors in utilizing the power to improve the accuracy of load forecasting. The proposed framework is implemented using the MATLAB platform and tested on CER smart meter data. The RMSE, MAE and MAPE benchmark parameters are considered for comparing the proposed framework with the existing state-of-the-art techniques, viz., LSTM dropout, GRU, SVR and ARIMA. From the experimental results, it is evident that the proposed framework has achieved a better prediction accuracy despite the dataset having different load profiles, huge number of customers, uncertainties and volatility. [ABSTRACT FROM AUTHOR]