Abstract
In this paper, we analyze the characteristics of the load forecasting task in the Energy Internet context and the deficiencies of existing methods and then propose a data driven approach for one-hour-ahead load forecasting based on the deep learning paradigm. The proposed scheme involves three aspects. First, we formulate a historical load matrix (HLM) with spatiotemporal correlation combined with the EI scenario and then create a three-dimensional historical load tensor (HLT) that contains the HLMs for multiple consecutive time points before the forecasted hour. Second, we preprocess the HLT leveraging a novel low rank decomposition algorithm and different load gradients, aiming to provide a forecasting model with richer input data. Third, we develop a deep forecasting framework (called the 3D CNN-GRU) featuring a feature learning module followed by a regression module, in which the 3D convolutional neural network (3D CNN) is used to extract the desired feature sequences with time attributes, while the gated recurrent unit (GRU) is responsible for mapping the sequences to the forecast values. By feeding the corresponding load label into the 3D CNN-GRU, our proposed scheme can carry out forecasting tasks for any zone covered by the HLM. The results of self-evaluation and a comparison with several state-of-the-art methods demonstrate the superiority of the proposed scheme.
Highlights
Accurate and stable load forecasting provides indispensable guidance for optimal unit commitment, efficient power distribution, and energy efficiency and plays a crucial role in power systems
(2) We developed a novel 3D convolutional neural network (3D convolutional neural network (CNN))-gated recurrent unit (GRU) model, which consists of two functional modules and can forecast the load trend of any zone covered by the historical load matrix (HLM) by changing the load label
For the RECO with a larger capacity, over 90% of the mean absolute error (MAE) was lower than 10 MW, in which nearly half of the results were less than 5 MW; while Figure 6a visually suggests the stability of ours, that is the root mean squared error (RMSE) curve generally deviated from MAE slightly
Summary
Accurate and stable load forecasting provides indispensable guidance for optimal unit commitment, efficient power distribution, and energy efficiency and plays a crucial role in power systems. The above mentioned deep models hope to rely on redundant connections to accommodate as many fluctuation patterns as possible, thereby improving the inference robustness This vision is difficult to achieve for complex and volatile electricity consumption problems, and these DL based methods often struggle with performance degradation elicited by two major problems: (1) The constructed input data usually fail to exploit historical load information fully. The scheme designs an input data plan based on historical load only and develops a deep model with excellent representation learning and regression capabilities, aiming to address the problems mentioned above as much as possible.
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