Day-Ahead Scheduling of Building Integrated Energy System Based on Feedforward Neural Network

WANG Zeye; LI Xueying; ZHOU Yujie; JING Tianjun

doi:10.3969/j.issn.1008-0198.2026.01.001

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Day-Ahead Scheduling of Building Integrated Energy System Based on Feedforward Neural Network

更新时间：2026-03-11

- Day-Ahead Scheduling of Building Integrated Energy System Based on Feedforward Neural Network
- Vol. 46, Issue 1, Pages: 1-7(2026)
- 作者机构：
  
  1. 中国农业大学信息与电气工程学院,北京,100083
  2. 太原理工大学电气与动力工程学院,山西,太原,030024
  3. 香港城市大学工学院,香港,999077
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2026.01.001
  CLC： TM714
- Online First：10 March 2026，
  
  Published：10 March 2026
- 稿件说明：
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王泽烨, 李雪莹, 周宇杰, 井天军. 基于前馈神经网络的楼宇综合能源系统日前调度[J]. 湖南电力, 2026, 46(1): 1-7.

WANG Zeye, LI Xueying, ZHOU Yujie, et al. Day-Ahead Scheduling of Building Integrated Energy System Based on Feedforward Neural Network[J]. 2026, 46(1): 1-7.
王泽烨, 李雪莹, 周宇杰, 井天军. 基于前馈神经网络的楼宇综合能源系统日前调度[J]. 湖南电力, 2026, 46(1): 1-7. DOI： 10.3969/j.issn.1008-0198.2026.01.001.

WANG Zeye, LI Xueying, ZHOU Yujie, et al. Day-Ahead Scheduling of Building Integrated Energy System Based on Feedforward Neural Network[J]. 2026, 46(1): 1-7. DOI： 10.3969/j.issn.1008-0198.2026.01.001.

摘要

随着楼宇综合能源系统(integrated energy system of building

BIES)的增多及其提升能源利用率的作用日益凸显

研究提升BIES内各设备耦合程度及与电网协调程度、降低BIES日综合运行成本具有重要意义。为此

提出基于前馈神经网络(feedforward neural network

FNN)的BIES日前优化调度模型。首先

分析含屋顶光伏的BIES的结构;其次

提出基于FNN的光伏出力日前预测方法及流程

为BIES优化调度提供可靠光伏发电数据;最后

以BIES购电成本、购气成本、设备运行维护成本组成的日综合运行成本最低为目标

提出基于BIES日前调度模型。算例结果表明

所提光伏出力预测方法能有效提高光伏发电出力预测精度

基于FNN的BIES日前优化调度模型能够有效提高BIES运行经济性。

Abstract

With the increase of building integrated energy systems(BIES) and its increasingly prominent role in improving energy utilization efficiency

it is necessary to study how to enhance the coupling degree of equipment within BIES and its coordination with the power grid

thereby reducing the daily comprehensive operating cost of BIES. Therefore

a BIES day-ahead optimization scheduling model based on feedforward neural network(FNN) is proposed. Firstly

the structure of BIES with rooftop photovoltaics is analyzed. Secondly

a day-ahead PV output prediction method and process based on FNN are proposed

providing reliable PV power generation data for BIES optimization scheduling. Finally

with the objective of minimizing the daily comprehensive operating cost composed of electricity purchase cost

gas purchase cost

and equipment operation and maintenance cost

a day-ahead scheduling model for BIES is proposed. The result of the calculation examples show that the proposed PV output prediction method can effectively improve the prediction accuracy of PV power generation output

and the propose day-ahead optimization scheduling model can effectively improve the operational economy of BIES.

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references

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