## Acknowledgements

The authors would like to thank INTERREG Caraïbes program for its financial support. The ETC (“Energy Transition in the Caribbean”) project is cofounded by the INTERREG Caraïbes program under the umbrella of the European Regional Development Fund (ERDF). This paper would not have been possible without their support.

The main author thanks Sergei Manzhos from Tokyo Tech for providing the Matlab code for High Dimensional Gaussian Processes (though not reported in this paper) and discussions, and Jérémy Rohmer from BRGM for in-depth explanations and discussions about Gaussian Processes.

The authors would like to thank the three anonymous reviewers.

## Appendix A. Gaussian processes: Theoretical background

To build the surrogates, we adopt the framework of “Gaussian Processes” (GP). We wrote this section using the reference textbook from Rasmussen and Williams [74]. The function $y(\chi)$ is assumed to be a realization of a GP $Y$. $Y$ is the sum of a deterministic, parametrized trend function $\mu(\chi)$, a centered square-integrable stochastic process $Z$ and some noise $\varepsilon$:

$$Y(\chi) = \sum_{k=1}^{p} \beta_k f_k(\chi) + Z(\chi) + \varepsilon \quad (40)$$

The $f_k$'s appearing in $\mu(\chi)$ are some fixed basis functions and the $\beta_k$'s are unknown real coefficients to be estimated. The noise is assumed to follow a normal distribution $\varepsilon \sim N(0, \sigma_n)$, where $\sigma_n$ is the noise standard deviation. Let us focus to the case where $\beta$ is set to a constant (“simple kriging”), so that the GP captures the whole variation of the output $y(\chi)$. We set the noise to a negligible value ($\sigma_n \ll 1$) since we assume the model is purely deterministic: there is no “nugget effect”. The GP construction is about deriving best linear predictions of $Y$. $Z$'s covariance function, named “kernel” and denoted $k(\cdot, \cdot)$, can be written as:

$$\forall \chi_1, \chi_2, k(\chi_1, \chi_2) = \sigma_f^2 R(\chi_1, \chi_2) = \operatorname{cov}(y_1, y_2) \quad (41)$$

Where 1 and 2 refer to two points of the training dataset, $\sigma_f$ is a scale parameter known as the noise-free signal standard deviation and $R(\cdot, \cdot)$ the correlation function related to $k(\cdot, \cdot)$. A prediction $\hat{Y}(\chi_{new})$ at a new observation point $\chi_{new}$ is:

$$\hat{Y}(\chi_{new}) = k_{new}^{\mathrm{T}} K^{-1} y \quad (42)$$

Where $k_{new} = k(\chi_{new})$ denotes the vector of covariances between the new point $\chi_{new}$ and the $N$ training points and $K = \sigma_f^2 R$ is the covariance matrix for the training points, and $y$ the output at the training points.

Kernels are functions parametrized by some “hyperparamaters”. A Matérn3/2 kernel function was used since it is once differentiable, making it good at capturing physical processes. It reads:

$$k(\chi_1, \chi_2) = \sigma_f^2 (1 + \sqrt{3} r) \exp(-\sqrt{3} r) \quad (43)$$

$$r = r(\chi_1, \chi_2) = \sqrt{\sum_{i=1}^{D} \left( \frac{\chi_i^{(1)} - \chi_i^{(2)}}{\ell_i} \right)^2} \quad (44)$$

Where $\ell_i$ ($i = 1, \dots, D$) is the correlation length for the predictor (variable) $i$. Such anisotropic kernels, i.e. kernels with independent correlation lengths per predictor, are sometimes referred to as “Automatic Relevance Determination” (ARD) kernels. The correlation lengths briefly define how far apart the input values $\chi_i$ can be for the response values to become uncorrelated. The GP is fitted with the built-in MATLAB fitrgp function. This function determines the most relevant set of hyperparameter (here the standard deviation $\sigma_f$ and correlations lengths $\ell_i$) by maximizing the Log Marginal Likelihood (LML) function [51] which reads as follows under simple kriging and no-nugget-effect assumptions:

$$\text{LML}(\sigma_f, \ell_1, \dots, \ell_D) = -\frac{1}{2}\log(\text{Tr}(K)) - \frac{1}{2}y^\mathrm{T}K^{-1}y - \frac{N}{2}\log(2\pi) \quad (45)$$

In Eqn 45, $\text{Tr}(K)$ accounts for the trace (i.e. the sum of elements on the main diagonal) of matrix $K$. Classical properties of Gaussian Processes include the fact that the estimation of the output at a new point $\hat{Y}(\chi)$ interpolates the data, provided that no nugget effect is included. Cross-validation is necessary to estimate the quality of the surrogate. Cross-validation is a procedure where a part $1/n$ of the data is randomly selected and left apart (here $n = 5$), acting as a test sample [55]. A surrogate is fitted on a fraction $(1-1/n)$ of the sample and the procedure is repeated $n$ times by permuting the samples. The metric for the estimation of the quality of the surrogate of a function $f$ is often chosen to be the Root Mean Square Error (RMSE):

$$\text{RMSE}(f) = \left( \sum_{i=1}^{N} \frac{[(f)_{\text{data}} - (f)_{\text{surrogate}}]^2}{N} \right)^{1/2} \quad (46)$$

The construction of GP with high dimensional data sets, which is the case here ($D = 17$), can be challenging [53,54]. The reader is referred to [52]
Acknowledgements

The authors would like to thank INTERREG Caraïbes program for its financial support. The ETC ("Energy Transition in the Caribbean") project is cofounded by the INTERREG Caraïbes program under the umbrella of the European Regional Development Fund (ERDF). This paper would not have been possible without their support.

The main author thanks Sergei Manzhos from Tokyo Tech for providing the Matlab code for High Dimensional Gaussian Processes (though not reported in this paper) and discussions, and Jérémy Rohmer from BRGM for in-depth explanations and discussions about Gaussian Processes.

The authors would like to thank the three anonymous reviewers.

Appendix A. Gaussian processes: Theoretical background

To build the surrogates, we adopt the framework of “Gaussian Processes” (GP). We wrote this section using the reference textbook from Rasmussen and Williams [74]. The function y(χ) is assumed to be a realization of a GP Y. Y is the sum of a deterministic, parametrized trend function μ(χ), a centered square-integrable stochastic process Z and some noise ε:

$$
Y(\chi) = \sum_{k=1}^{p} \beta_k f_k(\chi) + Z(\chi) + \varepsilon \quad (40)
$$

The $f_k$'s appearing in $\mu(\chi)$ are some fixed basis functions and the $\beta_k$'s are unknown real coefficients to be estimated. The noise is assumed to follow a normal distribution $\varepsilon \sim N(0, \sigma_n)$, where $\sigma_n$ is the noise standard deviation. Let us focus to the case where $\beta$ is set to a constant ("simple kriging"), so that the GP captures the whole variation of the output $y(\chi)$. We set the noise to a negligible value ($\sigma_n \ll 1$) since we assume the model is purely deterministic: there is no "nugget effect". The GP construction is about deriving best linear predictions of Y. Z's covariance function, named "kernel" and denoted $k(\cdot, \cdot)$, can be written as:

$$
\forall \chi_1, \chi_2, k(\chi_1, \chi_2) = \sigma_f^2 R(\chi_1, \chi_2) = \operatorname{cov}(y_1, y_2) \quad (41)
$$

Where 1 and 2 refer to two points of the training dataset, $\sigma_f$ is a scale parameter known as the noise-free signal standard deviation and $R(\cdot, \cdot)$ the correlation function related to $k(\cdot, \cdot)$. A prediction $\hat{Y}(\chi_{new})$ at a new observation point $\chi_{new}$ is:

$$
\hat{Y}(\chi_{\text{new}}) = k_{\text{new}}^{\mathrm{T}} \mathbf{K}^{-1} \mathbf{y} \quad (42)
$$

Where $k_{new} = k(\chi_{new})$ denotes the vector of covariances between the new point $\chi_{new}$ and the $N$ training points and $\mathbf{K} = \sigma_f^2 \mathbf{R}$ is the covariance matrix for the training points, and $\mathbf{y}$ the output at the training points.

Kernels are functions parametrized by some “hyperparamaters”. A Matérn3/2 kernel function was used since it is once differentiable, making it good at capturing physical processes. It reads:

$$
k(\chi_1, \chi_2) = \sigma_f^2 (1 + \sqrt{3} r) \exp(-\sqrt{3} r) \quad (43)
$$

with:
$$
r = r(\chi_1, \chi_2) = \sqrt{\sum_{i=1}^{D} \left( \frac{\chi_i^{(1)} - \chi_i^{(2)}}{\ell_i} \right)^2} \quad (44)
$$

Where $\ell_i$ ($i = 1, \dots, D$) is the correlation length for the predictor (variable) $i$. Such anisotropic kernels, i.e. kernels with independent correlation lengths per predictor, are sometimes referred to as “Automatic Relevance Determination” (ARD) kernels. The correlation lengths briefly define how far apart the input values $\chi_i$ can be for the response values to become uncorrelated. The GP is fitted with the built-in MATLAB *fitrgp* function. This function determines the most relevant set of hyperparameter (here the standard deviation $\sigma_f$ and correlations lengths $\ell_i$) by maximizing the Log Marginal Likelihood (LML) function [51] which reads as follows under simple kriging and no-nugget-effect assumptions:

$$
\text{LML}(\sigma_f, \ell_1, \dots, \ell_D) = -\frac{1}{2}\log(\text{Tr}(\mathbf{K})) - \frac{1}{2}\mathbf{y}^\mathrm{T}\mathbf{K}^{-1}\mathbf{y} - \frac{N}{2}\log(2\pi) \quad (45)
$$

In Eqn 45, Tr(K) accounts for the trace (i.e. the sum of elements on the main diagonal) of matrix K. Classical properties of Gaussian Processes include the fact that the estimation of the output at a new point Ŷ(χ) interpolates the data, provided that no nugget effect is included. Cross-validation is necessary to estimate the quality of the surrogate. Cross-validation is a procedure where a part 1/n of the data is randomly selected and left apart (here n = 5), acting as a test sample [55]. A surrogate is fitted on a fraction (1-1/n) of the sample and the procedure is repeated n times by permuting the samples. The metric for the estimation of the quality of the surrogate of a function f is often chosen to be the Root Mean Square Error (RMSE):

$$
RMSE(f) = \left( \sum_{i=1}^{N} \frac{[(f)_{\text{data}} - (f)_{\text{surrogate}}]^2}{N} \right)^{1/2} \quad (46)
$$

The construction of GP with high dimensional data sets, which is the case here (D = 17), can be challenging [53,54]. The reader is referred to [52]
for an overview of approaches to build high-dimensional GP and e.g. to [75] for methods to iteratively sample the function of interest.

## Appendix B. Interpretation of Gaussian Processes for $W_{el}^*$ and $Q_{sorp}^{ev}$

One advantage of GPs is their interpretability. Each variable *i* is associated with a correlation length $ℓ_i$ determined during the GP fitting (i.e. optimization of Eqn 45), which briefly defines how far apart the input values $χ_i$ can be for the response values to become uncorrelated (see Appendix A). In the case of simple kriging, smaller $ℓ_i$ are associated with a variable *i* having a larger influence. As $ℓ_i$ can change by several orders of magnitude, it is better to plot $\frac{1}{\sum_{i=1}^b 1/ℓ_i}$ to qualitatively compare the weights of each variable (see Fig. 19). For both $W_{el}^*$ and $Q_{sorp}^{ev}$, the specific geothermal flow rate $\dot{m}_{GTH}/\dot{Q}_{cooling}^{max}$, the geothermal temperature $T_{GTH}$ and the normalized nominal power of the absorption chiller $\dot{Q}_{sorp}^{ev}$ are the most significant input parameters. This is qualitatively in line with the sensitivity analysis reported in §3.3.

<figure><img src="image_40.png" /><figcaption>**Fig. 19.** Weight of each variable *i* upon (a) normalized electric consumption of the system $W_{el}^*$, (b) normalized amount of cooling produced by the absorption chiller $Q_{sorp}^{ev}$.</figcaption></figure>

## Appendix C. System characteristics on the trade-offs
for an overview of approaches to build high-dimensional GP and e.g. to [75] for methods to iteratively sample the function of interest.

**Appendix B. Interpretation of Gaussian Processes for $W_{el}^*$ and $Q_{sorp}^{ev}$**

One advantage of GPs is their interpretability. Each variable *i* is associated with a correlation length $ℓ_i$ determined during the GP fitting (i.e. optimization of Eqn 45), which briefly defines how far apart the input values $\chi_i$ can be for the response values to become uncorrelated (see Appendix A). In the case of simple kriging, smaller $ℓ_i$ are associated with a variable *i* having a larger influence. As $ℓ_i$ can change by several orders of magnitude, it is better to plot $\frac{1}{\sum_{i=1}^b 1/ℓ_i}$ to qualitatively compare the weights of each variable (see Fig. 19). For both $W_{el}^*$ and $Q_{sorp}^{ev}$, the specific geothermal flow rate $\dot{m}_{GTH}/\dot{Q}_{cooling}^{max}$, the geothermal temperature $T_{GTH}$ and the normalized nominal power of the absorption chiller $\dot{Q}_{sorp}^{ev}$ are the most significant input parameters. This is qualitatively in line with the sensitivity analysis reported in §3.3.

<figure><img src="image_46.png" /><figcaption>**Fig. 19.** Weight of each variable *i* upon (a) normalized electric consumption of the system $W_{el}^*$, (b) normalized amount of cooling produced by the absorption chiller $Q_{sorp}^{ev}$.</figcaption></figure>

**Appendix C. System characteristics on the trade-offs**
<table><thead><tr><th rowspan="2">Parameter</th><th colspan="6">Scenario parameters</th><th colspan="10">Design variables (i.e. results of optimization at the trade-off)</th><th colspan="2">Objectives</th></tr><tr><th>Q<sub>cooling</sub><sup>max</sup></th><th>T<sub>sp</sub> cooling</th><th>r<sub>DHW</sub></th><th>L</th><th>T<sub>GTH</sub></th><th>m&#775;<sub>GTH</sub></th><th>Δt<sub>5</sub></th><th>Q<sub>sorp</sub><sup>ev</sup></th><th>m&#775;<sub>air,CT</sub>/m&#775;<sub>w,CT</sub></th><th>m&#775;<sub>w,CT</sub>/m&#775;<sub>int</sub></th><th>udb<sub>2</sub></th><th>udb<sub>1</sub></th><th>dT<sub>sp</sub><sup>ev</sup></th><th>dT<sub>sp</sub><sup>CT</sup></th><th>udb<sub>4,1</sub></th><th>d<sub>casing</sub><sup>*</sup></th><th>Δρ</th><th>c</th><th>γ</th></tr></thead><tbody><tr><td>Unit</td><td>MW</td><td>°C</td><td>–</td><td>m</td><td>°C</td><td>ton.h<sup>-1</sup></td><td>h</td><td>–</td><td>–</td><td>–</td><td>°C</td><td>°C</td><td>°C</td><td>°C</td><td>°C</td><td>–</td><td>Pa.m<sup>-1</sup></td><td>€.MWh<sup>-1</sup></td><td>kgCO<sub>2</sub>eq.MWh<sup>-1</sup></td></tr><tr><td>Base.</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.77</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.00</td><td>3.00</td><td>–</td><td></td><td>37.5</td><td>103.1</td></tr><tr><td>A1</td><td>1.5</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.50</td><td>0.91</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.10</td><td>3.10</td><td>2.50</td><td>51.80</td><td>43.2</td><td>89.1</td></tr><tr><td>B1</td><td>2.0</td><td>7</td><td>1.0</td><td>1650</td><td>80</td><td>100</td><td>0.25</td><td>0.32</td><td>2.35</td><td>1.30</td><td>-0.50</td><td>6.57</td><td>0.00</td><td>2.00</td><td>3.00</td><td>2.50</td><td>62.24</td><td>41.9</td><td>130.6</td></tr><tr><td>B2</td><td>2.0</td><td>7</td><td>1.0</td><td>3300</td><td>80</td><td>100</td><td>0.25</td><td>0.25</td><td>2.44</td><td>1.27</td><td>-0.50</td><td>7.00</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>45.7</td><td>143.4</td></tr><tr><td>C3</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>85</td><td>100</td><td>1.57</td><td>0.81</td><td>3.00</td><td>0.55</td><td>-0.50</td><td>3.92</td><td>0.00</td><td>2.44</td><td>3.44</td><td></td><td></td><td>37.0</td><td>87.8</td></tr><tr><td>C4</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>90</td><td>100</td><td>1.68</td><td>0.81</td><td>3.00</td><td>0.59</td><td>-0.50</td><td>4.94</td><td>0.00</td><td>2.81</td><td>3.81</td><td></td><td></td><td>36.4</td><td>75.3</td></tr><tr><td>C5</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>95</td><td>100</td><td>1.77</td><td>0.80</td><td>3.00</td><td>0.62</td><td>-0.50</td><td>5.55</td><td>0.00</td><td>3.06</td><td>4.06</td><td></td><td></td><td>36.0</td><td>65.5</td></tr><tr><td>C6</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>100</td><td>100</td><td>1.89</td><td>0.80</td><td>3.00</td><td>0.61</td><td>-0.54</td><td>5.72</td><td>0.00</td><td>3.19</td><td>4.19</td><td></td><td></td><td>35.7</td><td>58.8</td></tr><tr><td>C7</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>105</td><td>100</td><td>2.08</td><td>0.83</td><td>3.00</td><td>0.53</td><td>-0.65</td><td>5.48</td><td>0.00</td><td>3.26</td><td>4.26</td><td></td><td></td><td>35.6</td><td>54.4</td></tr><tr><td>D1</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>125</td><td>1.57</td><td>0.85</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.01</td><td>3.01</td><td></td><td></td><td>37.8</td><td>95.0</td></tr><tr><td>D2</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>150</td><td>1.63</td><td>0.90</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.07</td><td>0.00</td><td>2.06</td><td>3.06</td><td></td><td></td><td>38.1</td><td>89.4</td></tr><tr><td>D3</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>175</td><td>1.66</td><td>0.89</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.23</td><td>0.00</td><td>2.09</td><td>3.09</td><td></td><td></td><td>38.0</td><td>85.9</td></tr><tr><td>D4</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>200</td><td>1.67</td><td>0.92</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.38</td><td>0.00</td><td>2.10</td><td>3.10</td><td></td><td></td><td>38.3</td><td>83.2</td></tr><tr><td>E1</td><td>2.0</td><td>7</td><td>0.0</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.74</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.49</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>43.3</td><td>118.9</td></tr><tr><td>E2</td><td>2.0</td><td>7</td><td>0.2</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.74</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.36</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>42.0</td><td>115.5</td></tr><tr><td>E3</td><td>2.0</td><td>7</td><td>0.4</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.75</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.24</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>40.8</td><td>112.3</td></tr><tr><td>E4</td><td>2.0</td><td>7</td><td>0.6</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.76</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.13</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>39.6</td><td>109.2</td></tr><tr><td>E5</td><td>2.0</td><td>7</td><td>0.8</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.77</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.02</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>38.6</td><td>105.8</td></tr><tr><td>F1</td><td>2.0</td><td>8</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.55</td><td>0.78</td><td>3.00</td><td>0.53</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>36.8</td><td>93.0</td></tr><tr><td>F2</td><td>2.0</td><td>9</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.74</td><td>0.80</td><td>3.00</td><td>0.51</td><td>-0.50</td><td>3.00</td><td>0.21</td><td>2.00</td><td>3.00</td><td></td><td></td><td>36.3</td><td>85.1</td></tr><tr><td>F3</td><td>2.0</td><td>10</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.93</td><td>0.81</td><td>3.00</td><td>0.50</td><td>-0.52</td><td>3.00</td><td>0.50</td><td>2.00</td><td>3.00</td><td></td><td></td><td>35.8</td><td>78.8</td></tr><tr><td>F4</td><td>2.0</td><td>11</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>2.17</td><td>0.80</td><td>3.00</td><td>0.50</td><td>-0.63</td><td>3.00</td><td>0.78</td><td>2.00</td><td>3.00</td><td></td><td></td><td>35.3</td><td>74.2</td></tr><tr><td>F5</td><td>2.0</td><td>12</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>2.33</td><td>0.79</td><td>3.00</td><td>0.50</td><td>-0.68</td><td>3.00</td><td>0.98</td><td>2.00</td><td>3.00</td><td></td><td></td><td>34.9</td><td>71.2</td></tr></tbody></table>
<table><thead><tr><th rowspan="2">Parameter</th><th colspan="6">Scenario parameters</th><th colspan="10">Design variables (i.e. results of optimization at the trade-off)</th><th colspan="2">Objectives</th></tr><tr><th>Q<sup>max</sup><sub>cooling</sub></th><th>T<sub>sp</sub> cooling</th><th>r<sub>DHW</sub></th><th>L</th><th>T<sub>GTH</sub></th><th>m&#775;<sub>GTH</sub></th><th>Δt<sub>5</sub></th><th>Q<sup>ev</sup><sub>sorp</sub><sup>*</sup></th><th>m&#775;<sup>max</sup><sub>air,CT</sub>/m&#775;<sub>w,CT</sub></th><th>m&#775;<sub>w,CT</sub>/m&#775;<sub>int</sub></th><th>udb<sub>2</sub></th><th>udb<sub>1</sub></th><th>dT<sup>ev</sup><sub>sp</sub></th><th>dT<sup>CT</sup><sub>sp</sub></th><th>udb<sub>4,1</sub></th><th>d<sub>casing</sub><sup>*</sup></th><th>Δρ</th><th>c</th><th>γ</th></tr></thead><tbody><tr><td>Unit</td><td>MW</td><td>°C</td><td>–</td><td>m</td><td>°C</td><td>ton.h<sup>-1</sup></td><td>h</td><td>–</td><td>–</td><td>–</td><td>°C</td><td>°C</td><td>°C</td><td>°C</td><td>°C</td><td>–</td><td>Pa.m<sup>-1</sup></td><td>€.MWh<sup>-1</sup></td><td>kgCO<sub>2</sub>eq.MWh<sup>-1</sup></td></tr><tr><td>Base.</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.77</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.00</td><td>3.00</td><td>–</td><td></td><td>37.5</td><td>103.1</td></tr><tr><td>A1</td><td>1.5</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.50</td><td>0.91</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.10</td><td>3.10</td><td>2.50</td><td>51.80</td><td>43.2</td><td>89.1</td></tr><tr><td>B1</td><td>2.0</td><td>7</td><td>1.0</td><td>1650</td><td>80</td><td>100</td><td>0.25</td><td>0.32</td><td>2.35</td><td>1.30</td><td>-0.50</td><td>6.57</td><td>0.00</td><td>2.00</td><td>3.00</td><td>2.50</td><td>62.24</td><td>41.9</td><td>130.6</td></tr><tr><td>B2</td><td>2.0</td><td>7</td><td>1.0</td><td>3300</td><td>80</td><td>100</td><td>0.25</td><td>0.25</td><td>2.44</td><td>1.27</td><td>-0.50</td><td>7.00</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>45.7</td><td>143.4</td></tr><tr><td>C3</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>85</td><td>100</td><td>1.57</td><td>0.81</td><td>3.00</td><td>0.55</td><td>-0.50</td><td>3.92</td><td>0.00</td><td>2.44</td><td>3.44</td><td></td><td></td><td>37.0</td><td>87.8</td></tr><tr><td>C4</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>90</td><td>100</td><td>1.68</td><td>0.81</td><td>3.00</td><td>0.59</td><td>-0.50</td><td>4.94</td><td>0.00</td><td>2.81</td><td>3.81</td><td></td><td></td><td>36.4</td><td>75.3</td></tr><tr><td>C5</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>95</td><td>100</td><td>1.77</td><td>0.80</td><td>3.00</td><td>0.62</td><td>-0.50</td><td>5.55</td><td>0.00</td><td>3.06</td><td>4.06</td><td></td><td></td><td>36.0</td><td>65.5</td></tr><tr><td>C6</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>100</td><td>100</td><td>1.89</td><td>0.80</td><td>3.00</td><td>0.61</td><td>-0.54</td><td>5.72</td><td>0.00</td><td>3.19</td><td>4.19</td><td></td><td></td><td>35.7</td><td>58.8</td></tr><tr><td>C7</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>105</td><td>100</td><td>2.08</td><td>0.83</td><td>3.00</td><td>0.53</td><td>-0.65</td><td>5.48</td><td>0.00</td><td>3.26</td><td>4.26</td><td></td><td></td><td>35.6</td><td>54.4</td></tr><tr><td>D1</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>125</td><td>1.57</td><td>0.85</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.01</td><td>3.01</td><td></td><td></td><td>37.8</td><td>95.0</td></tr><tr><td>D2</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>150</td><td>1.63</td><td>0.90</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.07</td><td>0.00</td><td>2.06</td><td>3.06</td><td></td><td></td><td>38.1</td><td>89.4</td></tr><tr><td>D3</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>175</td><td>1.66</td><td>0.89</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.23</td><td>0.00</td><td>2.09</td><td>3.09</td><td></td><td></td><td>38.0</td><td>85.9</td></tr><tr><td>D4</td><td>2.0</td><td>7</td><td>1.0</td><td>0</td><td>80</td><td>200</td><td>1.67</td><td>0.92</td><td>3.00</td><td>0.50</td><td>-0.50</td><td>3.38</td><td>0.00</td><td>2.10</td><td>3.10</td><td></td><td></td><td>38.3</td><td>83.2</td></tr><tr><td>E1</td><td>2.0</td><td>7</td><td>0.0</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.74</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.49</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>43.3</td><td>118.9</td></tr><tr><td>E2</td><td>2.0</td><td>7</td><td>0.2</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.74</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.36</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>42.0</td><td>115.5</td></tr><tr><td>E3</td><td>2.0</td><td>7</td><td>0.4</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.75</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.24</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>40.8</td><td>112.3</td></tr><tr><td>E4</td><td>2.0</td><td>7</td><td>0.6</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.76</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.13</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>39.6</td><td>109.2</td></tr><tr><td>E5</td><td>2.0</td><td>7</td><td>0.8</td><td>0</td><td>80</td><td>100</td><td>1.41</td><td>0.77</td><td>3.00</td><td>0.54</td><td>-0.50</td><td>3.02</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>38.6</td><td>105.8</td></tr><tr><td>F1</td><td>2.0</td><td>8</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.55</td><td>0.78</td><td>3.00</td><td>0.53</td><td>-0.50</td><td>3.00</td><td>0.00</td><td>2.00</td><td>3.00</td><td></td><td></td><td>36.8</td><td>93.0</td></tr><tr><td>F2</td><td>2.0</td><td>9</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.74</td><td>0.80</td><td>3.00</td><td>0.51</td><td>-0.50</td><td>3.00</td><td>0.21</td><td>2.00</td><td>3.00</td><td></td><td></td><td>36.3</td><td>85.1</td></tr><tr><td>F3</td><td>2.0</td><td>10</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>1.93</td><td>0.81</td><td>3.00</td><td>0.50</td><td>-0.52</td><td>3.00</td><td>0.50</td><td>2.00</td><td>3.00</td><td></td><td></td><td>35.8</td><td>78.8</td></tr><tr><td>F4</td><td>2.0</td><td>11</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>2.17</td><td>0.80</td><td>3.00</td><td>0.50</td><td>-0.63</td><td>3.00</td><td>0.78</td><td>2.00</td><td>3.00</td><td></td><td></td><td>35.3</td><td>74.2</td></tr><tr><td>F5</td><td>2.0</td><td>12</td><td>1.0</td><td>0</td><td>80</td><td>100</td><td>2.33</td><td>0.79</td><td>3.00</td><td>0.50</td><td>-0.68</td><td>3.00</td><td>0.98</td><td>2.00</td><td>3.00</td><td></td><td></td><td>34.9</td><td>71.2</td></tr></tbody></table>
# Appendix D. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apenergy.2024.123102.

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[46] Ruiz J, Navarro P, Hernández M, Lucas M, Kaiser AS. Thermal performance and emissions analysis of a new cooling tower prototype. Appl Therm Eng 2022;206: 118065. https://doi.org/10.1016/j.applthermaleng.2022.118065.

[47] Lucas M, Ruiz J, Martínez PJ, Kaiser AS, Viedma A, Zamora B. Experimental study on the performance of a mechanical cooling tower fitted with different types of water distribution systems and drift eliminators. Appl Therm Eng 2013;50:282-92. https://doi.org/10.1016/j.applthermaleng.2012.06.030.

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# Appendix D. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apenergy.2024.123102.

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