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Room thermal load prediction based on analytic hierarchy process and back-propagation neural networks

  • Research Article
  • Advances in Modeling and Simulation Tools
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Abstract

Accurate prediction of the heat load is the basic premise of intelligent regulation of the heating system, which helps to realize effective management of heating, ventilation, air conditioning system. For the problem that the weight of each influencing factor is not taken into account in the current heat load prediction and is not highly targeted, this article deeply explores the influence of different factors on the room heat load, and we propose a method to calculate room heat load prediction based on the combination of analytic hierarchy process (AHP) and back-propagation (BP) neural network. Firstly, eight environmental factors affecting the heat load are selected as prediction inputs through parametric analysis, and then the weights of each input are determined by AHP and normalize the prediction data by combining expert opinions, and finally do one-to-one training the quantified score and the room heat load to predict the future heat load by BP neural network. The simulation tests show that the mean absolute relative error (MARE) of the proposed prediction method is 5.40%. This article also verifies the influence of different expert opinions on the stability of the model. The results show that the proposed method can guarantee higher prediction accuracy and stability.

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Abbreviations

A :

judgment matrix [−]

a :

relative importance [−]

c :

number of data [−]

C.I :

consistency index [−]

C.R :

consistency ratio [−]

d :

rank difference between variables [−]

E :

comprehensive score [−]

R.I :

random consistency index [−]

S :

quantitative values matrix [−]

V :

comprehensive evaluation set [−]

v :

index [−]

W :

weights matrix [−]

α :

number of input neurons [−]

β :

number of output neurons [−]

Δ :

integer between 1 and 10 [−]

θ :

number of hidden layer neurons [−]

λ :

eigenvalues [−]

ρ :

rank correlation coefficient [−]

i, j :

1, 2, …, n

n :

matrix size

max:

maximum

o:

other expert

f:

first-level indicator

s:

secondary indicators

AHP:

analytic hierarchy process

BP:

back-propagation

HVAC:

heating, ventilation and air conditioning

KF:

Kalman filter

MAE:

mean absolute error

MARE:

mean absolute relative error

MLPM:

machine learning prediction methods

MPC:

model predictive control

NNPM:

neural network prediction methods

TFM:

traditional forecasting methods

SVM:

support vector machine

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Acknowledgements

This research was supported by the Natural Science Foundation of China (No. 61765012); the Natural Science Foundation of Inner Mongolia Autonomous Region (No. 2021LHBS05005); the Science and Technology Research Project of Inner Mongolia Autonomous Region Higher Education (No. 2021SHZR0620); the Inner Mongolia Autonomous Region 2017 Science and Technology Innovation Guidance Award Funding Projects (No. 2017CXYD-2); the Natural Science Foundation of Inner Mongolia Autonomous Region (No. 2019MS05008). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Authors and Affiliations

  1. College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China

    Xin Tan, Zhenjing Zhu, Guoxin Sun & Linfeng Wu

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  1. Xin Tan
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  2. Zhenjing Zhu
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  3. Guoxin Sun
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  4. Linfeng Wu
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Correspondence to Xin Tan.

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Tan, X., Zhu, Z., Sun, G. et al. Room thermal load prediction based on analytic hierarchy process and back-propagation neural networks. Build. Simul. 15, 1989–2002 (2022). https://doi.org/10.1007/s12273-022-0905-0

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  • DOI: https://doi.org/10.1007/s12273-022-0905-0

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