# Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

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## Abstract

**:**

## 1. Introduction

- Implementation of an ensemble-based DC energy prediction model that combines a set of individual neural network weak learners to forecast the DC energy demand for the next day and to refine it continuously considering four-hour intervals.
- Definition of energy flexibility in relation to the baseline load and a prediction model to forecast the potential DC energy flexibility to be used in DR programs.
- Implementation of a genetic heuristic to determine the optimal combination of the outcome of individual predictors to minimize the prediction error thus lowering the uncertainty concerning DR participation.

## 2. Related Work

## 3. DC Energy Prediction Model

#### 3.1. Demand Forecasting

- Day-ahead: energy values are forecasted for the next 24 h with a granularity of one hour;
- Intra-day: energy values are forecasted for the next 4 h with a granularity of half an hour;

- Season—the DC may consume/produce different quantities of energy depending on the season. For example, the energy consumption in summer can be higher than the energy consumption in winter especially due to more intensive use of cooling processes. Same reasoning may apply if we consider the renewable energy generation (i.e., solar energy). The possible values for this feature are: Spring, Summer, Autumn and Winter.
- Day of the week—a DC may consume different quantities of energy depending on the day of the week. For example, the energy consumption for Monday may be higher than the energy consumption in a weekend day such as Saturday if the DC is running banking tasks. Possible values for this feature are Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday.
- Weekend—a DC may consume different quantities of energy depending on whether it is weekend day or not.

#### 3.2. Flexibility Forecasting

#### 3.3. Genetic Algorithm Based Ensemble

## 4. Experimental Results

#### 4.1. DC Energy Demand Prediction Results

#### 4.2. DC Flexibility Forecasting Results

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 9.**Electrical energy historical data used in forecasting (orange—cooling sub-system & blue—IT servers’ sub-system).

**Figure 10.**Day-ahead energy demand predictions using MLP: (

**a**) IT servers and (

**b**) cooling sub-systems.

**Figure 11.**Day-ahead energy demand predictions using LSTM: (

**a**) IT servers and (

**b**) cooling sub-systems.

**Figure 12.**Day-ahead electrical energy demand predictions using ensemble model: (

**a**) IT servers and (

**b**) cooling sub-systems.

**Figure 13.**Average MAPE values different prediction model configurations: (

**a**) IT servers and (

**b**) cooling sub-systems.

**Figure 14.**Intra-day electrical energy demand predictions using MLP model: (

**a**) IT servers and (

**b**) cooling sub-systems.

**Figure 15.**Intra-day electrical energy demand predictions using LSTM model: (

**a**) IT servers and (

**b**) cooling sub-system.

**Figure 16.**Intra-day electrical energy demand predictions using ensemble model: (

**a**) IT servers and (

**b**) cooling sub-system.

**Figure 17.**Day-ahead and intra-day energy demand prediction results vs. actual monitored values ((

**a**)—detailed results day number 4, (

**b**)—MAE distribution on 5 days of testing data).

Sub-System | Characteristics |
---|---|

Cooling system | $Coefficient\text{}of\text{}Performance=\text{}3.5$ $Maximum\text{}Cooling\text{}Capacity\text{}=\text{}4000\text{}\mathrm{kWh}$ $Minimum\text{}Cooling\text{}Load\text{}=\text{}200\text{}\mathrm{kWh}$ $Maximum\text{}Cooling\text{}Load\text{}=\text{}2000\text{}\mathrm{kWh}$ $PUE\text{}=\text{}1.3$ |

IT servers | $No=9000,\text{}Type=\text{}Servers\text{}HP\text{}360\text{}DL$ $Maximum\text{}Power\text{}Consumption=3000\text{}\mathrm{kWh}$ $Delay\text{}Tolerant\text{}Workload\text{}=\text{}20\%$ |

DC Component | Time Frame | Prediction Model | No. Models | Contextual Features | No. Inputs | No. Neurons on Hidden Layer | No. Outputs |
---|---|---|---|---|---|---|---|

IT servers consumption | Day-ahead | MLP | 1 | isWeekend | 25 | 37 | 24 |

LSTM | 1 | isWeekend | 25 | 47 | 24 | ||

Intra-day | MLP | 6 | partOfDay | 9 | 20 | 8 | |

LSTM | 6 | partOfDay | 9 | 16 | 8 | ||

Cooling consumption | Day-ahead | MLP | 1 | isWeekend | 25 | 37 | 24 |

LSTM | 1 | isWeekend | 25 | 47 | 24 | ||

Intra-day | MLP | 6 | partOfDay | 9 | 20 | 8 | |

LSTM | 6 | partOfDay | 9 | 16 | 8 |

Prediction Model | Time Frame | Best MAPE Value [%] | |
---|---|---|---|

IT Servers Sub-System | Cooling Sub-System | ||

MLP | Day-ahead | 8.68 | 8.68 |

Intra-day | 8.05 | 8.09 | |

LSTM | Day-ahead | 8.37 | 8.50 |

Intra-day | 8.08 | 8.24 | |

Ensemble | Day-ahead | 8.15 | 8.09 |

Intra-day | 7.20 | 6.81 |

DC Sub-System | Prediction Type | Input Features | N | M | $\mathit{N}\mathit{u}\mathit{m}\mathit{b}\mathit{e}\mathit{r}\text{}\mathit{o}\mathit{f}\text{}\mathit{i}\mathit{n}\mathit{p}\mathit{u}\mathit{t}\mathit{s}$ | $\mathit{H}$ | ${\mathit{C}}_{\mathit{F}}$ | $\mathit{O}\mathit{u}\mathit{t}\mathit{p}\mathit{u}\mathit{t}\mathit{s}$ |
---|---|---|---|---|---|---|---|---|

IT servers | Day-ahead | Historical load Historical baseline Current baseline Contextual features | 24 | 24 | 77 | 100 | 5 | 24 |

Cooling system | Day-ahead | Historical load Historical baseline Current baseline Contextual features Server room flexibility | 24 | 24 | 101 | 120 | 5 | 24 |

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## Share and Cite

**MDPI and ACS Style**

Vesa, A.V.; Cioara, T.; Anghel, I.; Antal, M.; Pop, C.; Iancu, B.; Salomie, I.; Dadarlat, V.T.
Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs. *Sustainability* **2020**, *12*, 1417.
https://doi.org/10.3390/su12041417

**AMA Style**

Vesa AV, Cioara T, Anghel I, Antal M, Pop C, Iancu B, Salomie I, Dadarlat VT.
Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs. *Sustainability*. 2020; 12(4):1417.
https://doi.org/10.3390/su12041417

**Chicago/Turabian Style**

Vesa, Andreea Valeria, Tudor Cioara, Ionut Anghel, Marcel Antal, Claudia Pop, Bogdan Iancu, Ioan Salomie, and Vasile Teodor Dadarlat.
2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs" *Sustainability* 12, no. 4: 1417.
https://doi.org/10.3390/su12041417