Abstract
Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM.
Highlights
At present, the power system reform in China is underway, and the spot market in pilot provinces such as Guangdong and Zhejiang will be implemented [1]
The model proposed in this paper is compared with the other four deep learning models, and the details of models are as follows, in which model 5 is the abbreviation of the model proposed in this paper: Model 1 (GRU): The preprocessed data is input to the gated recurrent unit (GRU) layer directly, without using a convolution filter layer
long short-term memory (LSTM) layer hidden layer unit is 50; Model 3 (Conv-LSTM): The preprocessed data is input to the convolutional layer firstly for filtering, and two LSTM layers are used for prediction
Summary
The power system reform in China is underway, and the spot market in pilot provinces such as Guangdong and Zhejiang will be implemented [1]. Since the electricity spot market has the characteristics of complex trading varieties, high trading frequency, and fluctuating price, the forecasting level of ultra-short-term load is significant. Common intelligent prediction methods include support vector machine technology [9], neural network [10,11], random forest [12], etc These methods have strict requirements on the selection of features, requiring an experienced person to manually select the input features. The load forecasting model based on deep learning technology proposed in this paper can better process a large amount of historical data and extract key information. Through comparison with other models, the results show that the model proposed in this paper shows good overall performance in terms of accuracy and training time As a consequence, this model can reflect the fluctuations of ultra-short-term load in the future properly.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
