Integrated energy system planning research based on big data load prediction method

Yongli Wang,Suhang Yao,Jialin Yang,Pengxiang Zhao,Yuze Ma,Xichao Zhou,Zhen Li,Hekun Shen,Nan Wang,M.S Tahir,L Xing

doi:10.1051/e3sconf/202126701005

Yongli Wang, Suhang Yao + Show 9 more

Open Access

https://doi.org/10.1051/e3sconf/202126701005

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Abstract

The planning of integrated energy system is a very complex multi-target, multi-constraint, nonlinear, random uncertainty mixed integrated combination optimization problem, its planning and design process should not only consider the interdependence between the system capacity, energy conversion, energy storage, energy use and other links, but also consider the interaction and integration of cold, hot, electricity and other multi-energy flows, which is essentially a non-deterministic polynomial difficult problem. China’s energy continues to develop rapidly, all kinds of sensors and intelligent equipment data is increasing, the data obtained in the equipment and all kinds of sensors collected energy load prediction related factors such as temperature, weather, wind speed and other data volume increased dramatically, the data dimension is also increasing, the scale of data has also increased from GB to TB or even higher, based on the traditional prediction methods and intelligent prediction methods, has been far below the load forecast desired to achieve accuracy and speed requirements, Therefore, the use of big data technology to predict energy demand is an important future direction.

Highlights

With the development of big data technology, the highspeed processing of load curve data has been successfully realized, and energy consumption can be predicted in short-term time
A large number of researchers in the framework of big data technology to carry out load prediction, taking into account the integrated energy system in the big data environment of power load factors of multi-source, the temperature and other factors as factors, the use of twolayer multi-core learning algorithm, the establishment of support vector load prediction algorithm, is a comparative breakthrough [2].A large number of researchers carry out load prediction under the framework of big data technology
Using graph clustering algorithm to segment different characteristics of each industry users, on this basis, the big data analysis and processing technology combined with support vector machine algorithm applied to load prediction, designed a set of load prediction architecture [3], and made the implementation and comparative study of the algorithm, showing that the prediction results and the actual situation of high consistency, and the operating speed advantage is very obvious, has a strong practicality [4]

Summary

Introduction

With the development of big data technology, the highspeed processing of load curve data has been successfully realized, and energy consumption can be predicted in short-term time. A large number of researchers in the framework of big data technology to carry out load prediction, taking into account the integrated energy system in the big data environment of power load factors of multi-source, the temperature and other factors as factors, the use of twolayer multi-core learning algorithm, the establishment of support vector load prediction algorithm, is a comparative breakthrough [2].A large number of researchers carry out load prediction under the framework of big data technology. Considering the multi-source of power load factors in the integrated energy system, taking temperature and other factors as factors, using two-layer multi-core learning algorithm to establish support vector load prediction algorithm is a breakthrough.

Short-term power load prediction model based on attention mechanism

RIES can flow modeling

Integrated energy system planning objectives

Case validation

Conclusions