# Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens

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## Abstract

**:**

## 1. Introduction

## 2. State of the Art

#### 2.1. Electricity Management in Industrial Kitchens

#### 2.2. Electricity Consumption Forecasting

#### 2.3. Summary

## 3. Materials and Methods

#### 3.1. Forecasting Algorithms

#### 3.1.1. Prophet

#### 3.1.2. Random Forest

#### 3.1.3. Long Short-Term Memory Deep Neural Network

#### 3.2. Dataset

#### Data Preprocessing

#### 3.3. Performance Evaluation Methodology

#### 3.3.1. Training and Testing Procedures

#### 3.3.2. Performance Metrics

## 4. Results and Discussion

#### 4.1. Virtual Aggregate Forecast

#### 4.2. Individual Appliances Forecast

#### 4.3. Virtual Aggregate Forecast vs. Sum of Individual Loads Forecast

#### 4.4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

AE | Auto Encoder |

ARIMA | Auto Regressive Integrated Moving Average |

DL | Deep Learning |

DT | Decision Tree |

IK | Industrial Kitchen |

LSTM | Long Short-Term Memory |

MAE | Mean Absolute Error |

ML | Machine Learning |

MLP | Multi Layer Perceptron |

NRMSE | Normalized Root Mean Squared Error |

RES | Renewable Energy Sources |

RF | Random Forest |

RMSE | Root Mean Squared Error |

RNN | Recurrent Neural Network |

SVM | Support Vector Machine |

VA | Virtual Aggregate |

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Max Power (W) | Avg. Power (W) | Null Samples | |
---|---|---|---|

Blast Chiller | 5331 | 215 | 8056 (1.88%) |

Infrared Lights | 1133 | 211 | 147 (0.03%) |

Dish Washer | 4456 | 480 | 1949 (0.45%) |

Glass Washer | 1885 | 43 | 4 (0.001%) |

Convection Oven 1 | 8797 | 386 | 7044 (1.64%) |

Convection Oven 2 | 8337 | 382 | 7344 (1.71%) |

Salamander 1 | 3148 | 944 | 5995 (1.4%) |

Salamander 2 | 3182 | 526 | 6819 (1.59%) |

Dual Fryer | 5222 | 219 | 2619 (0.61%) |

Freezer | 1319 | 577 | 7936 (1.85%) |

Ice Machine | 421 | 143 | 7433 (1.73%) |

Mise en Place | 854 | 219 | 8073 (1.88%) |

Garde Manger 1 | 1278 | 180 | 8232 (1.92%) |

Garde Manger 2 | 418 | 70 | 8075 (1.88) |

Refrigerator—Drinks | 623 | 145 | 17 (0.004%) |

Refrigerator—Fish | 3709 | 96 | 107 (0.02%) |

Refrigerator—Meat | 1274 | 132 | 54 (0.01%) |

Refrigerator—Vegetables | 897 | 99 | 146 (0.03%) |

**Table 2.**Performance metrics of different algorithms’ comparison for the normalized virtual aggregate.

Prophet (H = 3D) | Prophet (H = 1 h) | RF | LSTM | |
---|---|---|---|---|

RMSE | 0.10 | 0.12 | 0.10 | 0.11 |

MAE | 0.073 | 0.068 | 0.066 | 0.066 |

Prophet (H = 3D) | Prophet (H = 1 h) | RF | LSTM | |
---|---|---|---|---|

Blast Chiller | 0.20 | 0.17 | 0.15 | 0.17 |

Infrared Lights | 0.31 | 0.26 | 0.24 | 0.25 |

Dish Washer | 0.20 | 0.19 | 0.20 | 0.21 |

Glass Washer | 0.06 | 0.05 | 0.0002 | 0.005 |

Convection Oven 1 | 0.14 | 0.13 | 0.14 | 0.14 |

Convection Oven 2 | 0.13 | 0.13 | 0.15 | 0.13 |

Salamander 1 | 0.34 | 0.25 | 0.25 | 0.22 |

Salamander 2 | 0.24 | 0.21 | 0.15 | 0.17 |

Dual Fryer | 0.08 | 0.08 | 0.08 | 0.08 |

Freezer | 0.28 | 0.12 | 0.097 | 0.10 |

Ice Machine | 0.31 | 0.31 | 0.29 | 0.31 |

Mise en Place | 0.34 | 0.33 | 0.26 | 0.28 |

Garde Manger 1 | 0.18 | 0.18 | 0.16 | 0.17 |

Garde Manger 2 | 0.19 | 0.19 | 0.18 | 0.19 |

Refrigerator—Drinks | 0.32 | 0.32 | 0.27 | 0.29 |

Refrigerator—Fish | 0.21 | 0.21 | 0.23 | 0.22 |

Refrigerator—Meat | 0.21 | 0.21 | 0.22 | 0.22 |

Refrigerator—Vegetables | 0.18 | 0.18 | 0.17 | 0.17 |

**Table 4.**Performance metrics of different algorithms’ comparison for the normalized virtual aggregate.

RMSE | MAE | |
---|---|---|

Prophet (H = 1 h)-Virtual Aggregate | 2290 | 1499 |

Prophet (H = 1 h)-Individual Appliances | 2287 | 1507 |

RF—Virtual Aggregate | 2230 | 1507 |

RF—Individual Appliances | 2520 | 1998 |

LSTM—Virtual Aggregate | 2461 | 1564 |

LSTM—Individual Appliances | 2630 | 1961 |

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**MDPI and ACS Style**

Amantegui, J.; Morais, H.; Pereira, L.
Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens. *Buildings* **2022**, *12*, 2231.
https://doi.org/10.3390/buildings12122231

**AMA Style**

Amantegui J, Morais H, Pereira L.
Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens. *Buildings*. 2022; 12(12):2231.
https://doi.org/10.3390/buildings12122231

**Chicago/Turabian Style**

Amantegui, Jorge, Hugo Morais, and Lucas Pereira.
2022. "Benchmark of Electricity Consumption Forecasting Methodologies Applied to Industrial Kitchens" *Buildings* 12, no. 12: 2231.
https://doi.org/10.3390/buildings12122231