Copula and stacked LSTM network coupled park multivariate load joint prediction method coupled with
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A forecasting method and stacked technology, applied in forecasting, biological neural network models, data processing applications, etc.
Active Publication Date: 2020-12-22
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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[0003] As a simple dynamic load forecasting method, although the load factor method originated in Japan can realize hourly forecasti
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Embodiment 1
[0045] refer to figure 1 , figure 2 and image 3 , which is the first embodiment of the present invention, provides a multi-element load joint forecasting method for parks that couples Copula and stacked LSTM networks, including:
[0046] S1: Collect historical data of cooling loads, heating loads, and electrical loads in typical seasons in the park, as well as temperature data and holiday information at corresponding time nodes. What needs to be explained is:
[0047] Collect historical data such as typical seasonal (summer) cooling load and electrical load, typical seasonal (winter) heating load and electrical load;
[0048] Collect temperature data and holiday information on corresponding time nodes;
[0049] Among them, the time resolution of cold, heat, and electric loads is 15 minutes, and the temperature data is the highest temperature throughout the day.
[0050] S2: Perform noise detection and repair on historical data and normalize data in combination with temp...
Embodiment 2
[0092] In order to better verify and explain the technical effects adopted in the method of the present invention, this embodiment chooses to compare and test the traditional two-dimensional output method of the classic LSTM model with the method of the present invention, and compare the test results by means of scientific demonstration to verify the present invention. The real effect of the invented method.
[0093] refer to Figure 4 , which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a prediction accuracy verification of a park multi-element load joint prediction method that couples Copula and stacked LSTM networks, including:
[0094] The stacked LSTM network is to pass the information flow solved by each LSTM layer to the next layer, and provide output at the last layer. The stacking mechanism deepens the extraction of sequence data information features. The stacked LSTM model framework is c...
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Abstract
The invention discloses a Copula and stacked LSTM network coupled park multivariate load joint prediction method. The method comprises the following steps: analyzing nonlinear correlation between cooling, heating and power multivariat loads as well as between the loads and other influence factors such as air temperature by utilizing a Copula theory, selecting input elements for load prediction, carrying out data division on the selected input elements to obtain a training set and a test set, and inputting the training set into a stacked LSTM deep neural network model; training the stacked LSTMdeep learning network model under a Keras environment deep learning framework, and storing weight information trained by the stacked LSTM deep learning network model; loading the trained stacked LSTMdeep neural network model to perform prediction simulation on the test set, and obtaining cold, heat and electric load data in a typical season; and using a mean absolute value error MAPE and a Taylor inequality coefficient TIC to evaluate and predict the cold, heat and electric load prediction results. The stacked LSTM deep learning network model is adopted to predict the load, and the park multivariate load can be effectively and accurately predicted.
Description
technical field [0001] The invention relates to the technical field of comprehensive energy load forecasting, in particular to a method for jointly forecasting multi-element loads in a park coupled with a Copula and a stacked LSTM network. Background technique [0002] With the proposal and development of emerging energy supply and demand systems such as distributed energy, integrated energy, and energy Internet, the dominant position of end users on the demand side, which has always been a passive recipient, has become increasingly prominent. Guided by the concept of "on-demand energy supply, sharing and integration", through in-depth mining of demand-side load characteristics and research on end-user energy consumption behavior, it is possible to understand users' personalized and differentiated service needs, promote interactive regulation of supply and demand, and improve Comprehensive energy efficiency of the system. Especially for regional integrated energy systems wi...
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