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Time Series Load Forecasting Using Pooling Based Multitasking Deep Neural Network

This paper presents a new Pooling based Multitasking Deep Neural Network (PMT-DNN) model with BiLSTM and dropout for individual customers- load forecasting. PMT-DNN learns the uncertainties in the smart meter dataset through multitasking. Customers are clustered based on their consumption similarity behaviour using agglomerative hierarchical clustering. Load profile pooling method is employed in each cluster to increase the volume of data and diversity to avoid over fitting. The framework is experimented on Irish CER real-time smart meter dataset of randomly selected 986 residential customers using MATLAB 2019b platform. Compared with LSTM dropout and pooling based LSTM method, the proposed PMT-DNN framework outperforms in terms of prediction accuracy, input data learning and convergence rate. In addition, the evaluation parameters viz., RAISE, MAE and MAPE values are also significantly improved.