# Evaluation of Energy Consumption in German Hospitals: Benchmarking in the Public Sector

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}, 14.37 MWh/worker, and 23.41 MWh/bed. The indicator dependent on the number of beds proved to be the most suitable as a reference to quantify the energy consumption of a hospital.

## 1. Introduction

^{2}per year.

## 2. Results

#### 2.1. Morphological Analysis of Hospitals

#### 2.2. Correlation between Average Annual Energy Consumption and Built Surface Area, Number of Workers, and Number of Beds

^{2}= 0.8983) between the variables is observed.

^{2}.

^{2}= 0.9106). It can, therefore, be concluded that there is a dependent relationship between the number of workers and the average annual energy consumption.

^{2}= 0.9223. A high correlation was found between energy consumption and the number of beds in a hospital—the highest value of the studies conducted.

#### 2.3. Results of the Statistical ANOVA

#### 2.3.1. Study of Energy Consumption per HCNB

#### 2.3.2. Energy Consumption as Related to GDP

_{Surface}0.01) and the number of beds (p

_{Beds}0.04), and no statistical significance according to the number of workers (p

_{Workers}0.43). Therefore, it can be concluded that there is a direct relationship between the energy consumption based on the number of beds in a hospital. It is interesting to note that of the three GDP groups analyzed with respect to the surface indicator, the average energy consumption values obtained were very similar in the hospitals of GDP1 (0.19 MWh/m

^{2}) and GDP2 (0.24 MWh/m

^{2}). However, this energy consumption increases considerably in the case of hospitals within the GDP3, where a value of 0.45 MWh/m

^{2}is obtained. This increase also occurs in the analysis of the number of beds. In this case, the data obtained were 26.83 MWh/m

^{2}for GDP1, 20.25 MWh/m

^{2}for GDP2 and 35.28 MWh/m

^{2}for GDP3.

#### 2.3.3. Energy Consumption as Related GL

_{Surface}0.04) in relation with the factor that takes into account the geographical location factor, unlike the consumption/number workers (p

_{Workers}0.74) or consumption/number beds (p

_{Beds}0.11), where no significance is observed. Therefore, geographical location indeed influences the energy consumption in the hospitals analyzed.

#### 2.3.4. Study of Energy Consumption in Time Intervals

_{Beds}0.02) but it did not exist for the indicators related to the built area of the hospital (p

_{Surface}0.59) and the number of workers (p

_{Workers}0.64).

#### 2.3.5. Indicators of Average Annual Energy Consumption in Public Hospitals in Germany

^{2}, 14.37 MWh/worker, and 23.41 MWh/bed.

## 3. Discussion

## 4. Materials and Methods

_{max}is the maximum daily temperature, X

_{c}is a logical coefficient that is worth 1 when the average daily temperature is lower than 15 °C and 0 when it is higher and T

_{min}is the minimum daily temperature.

_{max}is the maximum daily temperature, Ni is the number of days of the month considered, and X

_{c}is a logical coefficient that is worth 1 when the average daily temperature is higher than 20 °C and 0 when it is lower

## 5. Conclusions

^{2}, 14.37 MWh per worker, and 23.41 MWh per bed.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**The relation between the number of beds and the number of workers for the hospitals under study.

**Figure 3.**The relationship between the average annual energy consumption and the built area per hospital.

**Figure 7.**Average consumption in MWh of energy for each indicator: (

**a**) built surface area (MWh/m

^{2}), (

**b**) the number of workers (MWh/worker), and (

**c**) the number of beds (MWh/bed).

Factors | Distribution Regarding Factors |
---|---|

Hospital category based on number of beds (HCNB) | HCNB 1: <200 beds |

HCNB 2: 200–500 beds | |

HCNB 3: 500–1000 beds | |

HCNB 4: >1000 beds | |

Gross domestic product (GDP) | GDP 1: €35,001–€40,000 |

GDP 2: €40,001–€45,000 | |

GDP 3: >€45,001 | |

Heating degrees-day year (HDDY) | HDDY 1: 1000–1250 °C |

HDDY 2: 1250–1500 °C | |

HDDY 3: 1500–1750 °C | |

HDDY 4: 1750–2000 °C | |

HDDY 5: 2000–2250 °C | |

HDDY 6: 2250–2500 °C | |

HDDY 7: >2501 °C | |

Cooling degrees-day year (CDDY) | CDDY 1: ≤100 °C |

CDDY 2: 101–200 °C | |

CDDY 3: 201–300 °C | |

CDDY 4: 301–400 °C | |

CDDY 5: >401 °C | |

Geographic location (GL) | Bavaria |

Baden-Württemberg | |

Bremen | |

North Rhine-Westphalia | |

Hessen | |

Range of years | 2005–2009 |

2010–2015 |

**Table 2.**Results for the p-value corresponding to the analysis of variance (ANOVA) for the average annual energy consumption.

Indicators Consumption Ratios | Experiments Factors (p-Value) | |||||
---|---|---|---|---|---|---|

HCNB | GDP | HDDY | CDDY | GL | Period 2005–2009 and 2010–2015 | |

$\frac{MWhmeanenergyconsumption}{{m}^{2}builtsurfacearea}$ | 0.44 | 0.01 * | 0.18 | 0.14 | 0.04 * | 0.54 |

$\frac{MWhmeanenergyconsumption}{numberofworkers}$ | 0.00 * | 0.43 | 0.24 | 0.19 | 0.74 | 0.64 |

$\frac{MWhmeanenergyconsumption}{numberofbeds}$ | 0.01 * | 0.04 * | 0.25 | 0.17 | 0.11 | 0.02 * |

Hospital Category | MeanDiff | SEM | t Value | Prob | Sig. | LCL | UCL |
---|---|---|---|---|---|---|---|

HCNB 1–HCNB 2 | −2.52 | 4.09 | −0.62 | 0.54 | 0 | −11.11 | 6.07 |

HCNB 3–HCNB 2 | 9.14 | 4.20 | 2.18 | 0.04 | 1 | 0.32 | 17.96 |

HCNB 3–HCNB 1 | 11.66 | 3.71 | 3.14 | 0.01 | 1 | 3.87 | 19.46 |

HCNB 4–HCNB 2 | 14.54 | 6.00 | 2.42 | 0.03 | 1 | 1.93 | 27.14 |

HCNB 4–HCNB 1 | 17.06 | 5.67 | 3.01 | 0.01 | 1 | 5.15 | 28.97 |

HCNB 4–HCNB 3 | 5.39 | 5.75 | 0.94 | 0.36 | 0 | −6.68 | 17.47 |

Hospital | GL | Climatic Zone [36] | Hospital Category (HCNB) | GDP | HDDY | CDDY |
---|---|---|---|---|---|---|

Bezirkskrankenhaus-Kaufbeuren | Bavaria | 15 | HCNB 2 | GDP 2 | HDDY 4 | CDDY 2 |

Klinik Immenstadt | 15 | HCNB 1 | HDDY 5 | CDDY 2 | ||

Klinik Sonthofen | 15 | HCNB 1 | HDDY 5 | CDDY 3 | ||

Klinik Oberstdorf | 15 | HCNB 1 | HDDY 5 | CDDY 2 | ||

KH Rotthalmünster | 13 | HCNB 1 | HDDY 3 | CDDY 2 | ||

KH Vilshofen | 13 | HCNB 1 | HDDY 5 | CDDY 3 | ||

KH Wegscheid | 10 | HCNB 1 | HDDY 3 | CDDY 3 | ||

Klinikum Bogenhausen | 13 | HCNB 3 | HDDY 4 | CDDY 2 | ||

Klinikum Harlaching | 13 | HCNB 3 | HDDY 4 | CDDY 2 | ||

Klinikum Neuperlach | 13 | HCNB 3 | HDDY 4 | CDDY 2 | ||

Klinikum Schwabing | 13 | HCNB 3 | HDDY 4 | CDDY 2 | ||

Klinik Thalkirchen Strasse | 13 | HCNB 1 | HDDY 4 | CDDY 2 | ||

Klinikum Karlsruhe | Baden-Württemberg | 12 | HCNB 4 | GDP 2 | HDDY 3 | CDDY 5 |

Psychiatric Clinics | 12 | HCNB 2 | HDDY 3 | CDDY 5 | ||

Klinikum Bad-Hersfeld | Hessen | 7 | HCNB 3 | GDP 1 | HDDY 5 | CDDY 5 |

Orthopädie Bad-Hersfeld | 7 | HCNB 1 | HDDY 5 | CDDY 5 | ||

Klinikum Bremen-Mitte | Bremen | 3 | HCNB 4 | GDP 3 | HDDY 4 | CDDY 5 |

Diakonie-Krankenhaus | 3 | HCNB 2 | HDDY 4 | CDDY 5 | ||

LWL-Klinik Münster | North Rhine-Westphalia | 5 | HCNB 2 | GDP 1 | HDDY 3 | CDDY 1 |

LVR-Klinikums Düsseldorf | 5 | HCNB 3 | HDDY 3 | CDDY 1 | ||

LVR Klinik Bedburg-Hau | 5 | HCNB 4 | HDDY 3 | CDDY 5 | ||

LVR Kliniken at the Standort Viersen | 5 | HCNB 3 | HDDY 3 | CDDY 5 | ||

LWL-Klinik Lengerich | 5 | HCNB 2 | HDDY 3 | CDDY 1 |

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**MDPI and ACS Style**

González González, A.; García-Sanz-Calcedo, J.; Rodríguez Salgado, D. Evaluation of Energy Consumption in German Hospitals: Benchmarking in the Public Sector. *Energies* **2018**, *11*, 2279.
https://doi.org/10.3390/en11092279

**AMA Style**

González González A, García-Sanz-Calcedo J, Rodríguez Salgado D. Evaluation of Energy Consumption in German Hospitals: Benchmarking in the Public Sector. *Energies*. 2018; 11(9):2279.
https://doi.org/10.3390/en11092279

**Chicago/Turabian Style**

González González, Alfonso, Justo García-Sanz-Calcedo, and David Rodríguez Salgado. 2018. "Evaluation of Energy Consumption in German Hospitals: Benchmarking in the Public Sector" *Energies* 11, no. 9: 2279.
https://doi.org/10.3390/en11092279