Multivariate load prediction method for user-level comprehensive energy system

Abstract

The invention discloses a multivariate load prediction method for a user-level comprehensive energy system. The method comprises steps of carrying out ntrinsic mode decomposition on the electrical load, the cold load and the thermal load through complete ensemble empirical mode decomposition of self-adaptive noise, and decomposing strong non-stationary components obtained through decomposition again through variational mode decomposition; extracting principal components from the weather calendar rule feature set based on kernel principal component analysis to realize data dimension reduction;and forming an input set by using the decomposed non-stationary and stationary sequence components and principal components extracted from the weather calendar rule feature set respectively, performing prediction by using a DBiLSTM neural network and an MLR respectively, and finally reconstructing a prediction result to obtain a final prediction result. According to the method, the coupling characteristic of the multi-element load in the user-level comprehensive energy system can be deeply mined, the multi-element load prediction precision of the user-level comprehensive energy system is effectively improved, and a very good prediction effect can also be achieved for the multi-element load with randomness and large volatility.

Description

technical field [0001] The invention relates to the field of multi-element load forecasting of integrated energy systems, in particular to a user-level integrated energy system multi-element load forecasting method. Background technique [0002] In order to solve the energy shortage and realize precise energy management, the Integrated Energy System (IES) came into being. A variety of energy sources are supplied and converted in the IES, so that there is a certain coupling relationship among the electricity, cooling and heating loads. With the development of the electricity market and the application of energy management systems, higher requirements are put forward for load forecasting. [0003] Traditional load forecasting methods are statistical methods and machine learning methods, including Kalman filter, multiple linear regression (Multiple Linear Regression, MLR), support vector regression, random forest, etc. Statistical methods have a simple model and fast predicti...

AI Technical Summary

Problems solved by technology

[0005] However, most of the current research methods only predict the power load, and the single load forecasting method ignores the coupling characteristics between multiple loads, and cannot accurately predict IES multiple loads.

At present, there are few researches on IES multi-element load forecasting, and most of the current research is on regional-level IES. For user-level IES, multi-element loads are random and more volatile, and load forecasting faces greater challenges.

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