# Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review

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## Abstract

**:**

_{2}emissions. Optimization of refrigeration systems is often a complex and time-consuming problem. This is where technologies such as big data and artificial intelligence play an important role. Nowadays, smart sensorization and the development of IoT (Internet of Things) make the massive connection of all kinds of devices possible, thereby enabling a new way of data acquisition. In this scenario, refrigeration systems can be measured comprehensively by acquiring large volumes of data in real-time. Then, artificial neural network (ANN) models can use the data to drive autonomous decision-making to build more efficient refrigeration systems.

## 1. Introduction

## 2. Paradigm Shift—Industry 4.0

## 3. Artificial Neural Networks in Industrial Refrigeration Systems

#### 3.1. Artificial Neural Networks

#### 3.1.1. Refrigeration System Modeling Using ANNs

#### 3.1.2. Refrigerant Properties Modeling Using ANNs

^{2}results close to 1.

#### 3.1.3. Fault Diagnosis in Refrigeration Systems Using ANNs

#### 3.2. Deep Neural Networks

#### 3.2.1. Refrigeration System Modeling Using DNNs

^{2}equals to 0.97. The linear regression model, however, performs poorly because the algorithm is not able to capture the non-linearity of the system.

#### 3.2.2. Fault Diagnosis in Refrigeration System Using DNNs

#### 3.3. Convolutional Neural Networks

- 0
- Convolutional layer: It is composed of convolutional filters. Its main function is the extraction of spatial features from the input signals. Convolution operations produce new signals that may reveal more information about the input than the original signal itself.
- 1
- Pooling layer: Also called the sub-sampling layer. The main function of this layer is to reduce the dimensionality of the signals of the previous layers. The pooling layer has a beneficial effect on network performance by reducing overfitting and training time. It also reduces the noise of the input data.
- 2
- Fully connected layer: This layer is often used at the end of a CNN. It contains artificial neurons that calculate the output of the network based on the output of the previous layers.

#### 3.3.1. Refrigeration System Modeling Using CNNs

#### 3.3.2. Fault Diagnosis in Refrigeration System Using CNNs

#### 3.4. Recurrent Neural Networks

#### 3.4.1. Refrigeration System Modeling Using LSTM Networks

#### 3.4.2. Fault Diagnosis in Refrigeration Systems Using LSTM Networks

## 4. Discussion and Trend Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 2.**Evolution of the number of studied articles that apply artificial neural networks to refrigeration systems.

**Figure 3.**Deep neural network architecture [51].

**Figure 4.**Basic structure of a CNN [53].

**Figure 5.**Fault diagnostic system using CNNs [65].

**Figure 6.**Time series transformation for LSTM models [79].

Hyper-Parameter | Description |
---|---|

Learning Rate (LR) | Probably the most important hyper-parameter of an ANN [25]. This parameter determines how fast the model adapts to the problem. A high LR leads to rapid convergence of the model, however, it may offer a sub-optimal solution; while with a low LR, the model may become stuck and not find the optimal solution. |

Optimizer | These are the algorithms used for training the ANN. They are used to adjust parameters such as the weights or biases of the neurons minimizing the error. Some of them are Gradient Descent, Adagrad, Adadelta, or Adam. |

Batch size | The batch size determines the number of training data points in each mini batch. It has a direct impact on network training time and performance. Large batch sizes reduce training time, but may introduce instabilities in the process causing the network to under-generalize the problem [26]. |

Epochs | The number of iterations the training algorithm will perform. One epoch consists of one full pass of training over the entire dataset. More iterations may reduce the error of the network, but we may be over-training the model. |

Output Variable | Input Variables | Refs. |
---|---|---|

COP | Evaporation temperature | [20] |

Mass flow rate, condensation temperature | [29] | |

Mass flow rate, refrigerant temperatures, compressor speed | [30] | |

Mass flow rate, evaporation and condensation temperature, compressor speed | [31] | |

Evaporation and condensation temperatures, pressure in evaporator, pressure in condenser, cooling load | [32] | |

Evaporation and condensation temperatures, cooling load | [33] | |

Refrigerant temperatures, compressor speed, refrigerant flow rate | [34] | |

Temperatures in evaporator and condenser | [35,36,37] | |

Air temperature, air relative humidity | [38] | |

Power consumption | Mass flow rate, temperature in condenser | [29] |

Temperatures and pressure in evaporator and condenser, cooling load | [32] | |

Temperatures in evaporator and condenser | [36] | |

Air temperature, air relative humidity | [38] | |

Temperature in evaporator, pressure in evaporator | [39] | |

Air temperature, compressor speed | [40] | |

Air temperature | [41] | |

Compressor speed | [42] | |

Mass flow rate | Mass flow rate, temperature in condenser | [29] |

Temperature and pressure in evaporator and condenser, cooling load | [32] | |

Temperature of refrigerant, refrigerant flow rate | [43] | |

Cooling capacity | Temperatures in evaporator and condenser | [36] |

Air temperature, air relative humidity | [38] | |

Compressor speed | [42] | |

Expansion valve | Temperature and pressure in evaporator | [39,44] |

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**MDPI and ACS Style**

Pérez-Gomariz, M.; López-Gómez, A.; Cerdán-Cartagena, F.
Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review. *Clean Technol.* **2023**, *5*, 116-136.
https://doi.org/10.3390/cleantechnol5010007

**AMA Style**

Pérez-Gomariz M, López-Gómez A, Cerdán-Cartagena F.
Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review. *Clean Technologies*. 2023; 5(1):116-136.
https://doi.org/10.3390/cleantechnol5010007

**Chicago/Turabian Style**

Pérez-Gomariz, Mario, Antonio López-Gómez, and Fernando Cerdán-Cartagena.
2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review" *Clean Technologies* 5, no. 1: 116-136.
https://doi.org/10.3390/cleantechnol5010007