Abstract
Precise and reliable forecasting of short-term electricity load is essential to the development of smart grids. Particularly, deep neural networks (DNNs) are widely utilized for the prediction of short-term electricity load due to their automatic feature extraction ability. However, these available stacked deep-learning models may lose some temporal features or spatial features of original input data. To capture more comprehensive information, in this article, we present an integration scheme based on empirical mode decomposition (EMD), similar day methods, and DNNs to perform short-term load forecasting. It is especially worth noting that the electricity price is also an important factor for load variation, which is considered in our proposed scheme. Specifically, there are two primary layers: a feature extraction layer and a forecasting layer. In the feature extraction layer, EMD is applied to decompose load time series into several components, which are arranged into the 2-D input matrix of the convolutional neural network (CNN). Both the output vectors of the CNN and the raw load sequences are fed into the long short-term memory (LSTM) layer. Therefore, the whole EMD based CNN-LSTM approach extracts multimodal spatial-temporal features from input data. Meanwhile, the electricity price data is utilized to obtain multimodal spatial-temporal features in the same way. Additionally, the day and hour information and loads of similar days are to augment extra features for prediction. In the forecasting layer, the forecasting task is accomplished through a fully-connected neural network based on the outputs of the feature extraction layer. Leveraging these techniques enables our proposed scheme to extract more latent features, which significantly improve the accuracy. In order to demonstrate the performance of our proposed scheme, related experiments are conducted on actual data from the electricity market in Singapore. Compared to other available models, our proposed scheme is superior in graphic and numerical results.
Highlights
Load forecasting is of crucial importance for the reliable operation of power systems, which plays an essential role in energy management, economic dispatch, and maintenance planning in power grids [1], [2]
Since long short-term memory (LSTM) is trained by the extracted feature vector from convolutional neural network (CNN), the short-term load forecasting (STLF) based on above two CNN-LSTM models may lose some important temporal feature or spatial feature information of original input data
We present an empirical mode decomposition (EMD) based CNN-LSTM approach that is able to extract multimodal spatial-temporal features in electricity load/price time series
Summary
Load forecasting is of crucial importance for the reliable operation of power systems, which plays an essential role in energy management, economic dispatch, and maintenance planning in power grids [1], [2]. RNN and its variants are effective in handling time series data, especially in extracting the dynamic temporal information [23] Based on these observations, an emerging model combining CNN and LSTM (CNNLSTM) to learn both spatial and temporal characteristics of input data has been presented [29]–[32]. Since LSTM is trained by the extracted feature vector from CNN, the STLF based on above two CNN-LSTM models may lose some important temporal feature or spatial feature information of original input data. We present an EMD based CNN-LSTM approach that is able to extract multimodal spatial-temporal features in electricity load/price time series. Rf , Ri, Ro, and Rc are the weight matrices associated with the current input v. bf , bi, bo, and bc are the corresponding bias vectors. · represents the matmul product, and represents the element-wise multiplication. δ(·) denotes the sigmoid activation function, and φ(·) denotes the both hyperbolic tangent activation function
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