**2.3 Our methodology**

The present work reports an experiment performed using a simple LCOE model, built according to basic methodology proposed by IEA. The performed experiment is simple and straightforward. Two energy scenarios were produced, one based on a certain hypothesis of change in the fuel cost, the other based on a hypothesis of change in fuel cost, O&M cost, and CO2 price, for the CCGT type plant, over a period of 30 years.

In each scenario, a certain LCOE profile is obtained for the time horizon considered. A simple regression analysis is then performed on this variable, using as explanatory variables, first the cost of fuel, and then the operating costs.

The analysis is carried out both using a LM and the SVM, with further manual tuning of the last to improve its performance. The manual tuning for SVR was used for the sake of simplicity since the main goal of the study is to suggest the application of this ML technique to gain forecasting accuracy to use in the following phase, the cost-effectiveness analysis.<sup>4</sup>

To evaluate the accuracy of the forecast, the Root Mean Square Error (RMSE), the Mean Average Error (MAE) and the Mean Average Percentage Error (MAPE) were used.<sup>5</sup>

This simple test was performed to show the accuracy of the fuel cost and O&M cost as a predictor of CCGT LCOE.

Once established the best technique, the data from the two scenarios in a third scenario are modified, under certain hypothesis explained in the follows, to made a C-E A between a technology represented by IEA data and another of the same type with little changes in O&M costs. Using ICER as a winning criterion, it is possible to select the best energy generation option and, finally, to trace the corresponding CO2 emission estimate trend over the plant's lifetime.

All the data coming from IEA [2].

The LCOE model.

First, a LCOE model based on IEA Eq. (1), with the following level of detail, was built.

The basic relationships of the model are:

$$PF = Power \* 8760 \* AVLF \* \frac{AAF}{100} \* (1 - Au\varkappa P) \tag{2}$$

$$
\hbar w \mathfrak{s} = \mathbf{1} - wd \tag{3}
$$

$$k\mathbf{s} = k\mathbf{y}\mathbf{\hat{t}} + \mathbf{E}\mathbf{M}\mathbf{R}\mathbf{P} \ast \mathbf{B} \tag{4}$$

$$i = wd \ast kd + ws \ast ks \tag{5}$$

$$d = i/(\mathbf{1} + i) \tag{6}$$

$$d\hat{\mathbf{r}} = \sum\_{j} \mathbf{1}/(\mathbf{1} + \mathbf{i})^{j} \tag{7}$$

$$\text{[cifinal} = \text{iofinal} + \left(\frac{\text{ic}}{\text{CnsT}}\right) \* d\text{fi} \tag{8}$$

$$df = \sum\_{j} \mathbf{1}/(\mathbf{1} + d)^{j} \tag{9}$$

<sup>4</sup> Indeed, manual tuning is often considered as one of the most significant choice [18].

<sup>5</sup> See [19] for a complete discussion about the used metrics.

*Machine Learning in Estimating CO2 Emissions from Electricity Generation DOI: http://dx.doi.org/10.5772/intechopen.97452*

$$\text{icfinal} = \text{icfinal} + \left(\frac{\text{ic}}{\text{CnsT}}\right) \* \text{dbf1} \tag{10}$$

$$d\mathbf{f}\mathbf{i} = \sum\_{j} \mathbf{1}/(\mathbf{1}+\mathbf{i})^{j} \tag{11}$$

$$Pro = Pro + PF\*df\tag{12}$$

$$\text{OM} = ((\text{FOM} + \text{VOM}) \ast \text{PF}) \ast df \tag{13}$$

$$Fue = ((CFue)\*PF)\*df\tag{14}$$

$$\text{CO2} = ((\text{PCO2}) \* \text{PF}) \* df \tag{15}$$

$$\text{Cost} = \sum\_{j} (\text{OM} + \text{Fue} + \text{CO2}) \tag{16}$$

$$Decom = n \ast Dem \ast Pro\tag{17}$$

$$LCOE = (Power \ast \text{inf} \, \text{rad} \, \ast \, \text{1000} \, \cdot \text{Cost} + December) / Pr \tag{18}$$

Where:


All other parameters are settled using the IEA values.


#### **Table 1.**

*Scenarios used for the regression of LCOE on fuel cost and O&M cost*

We have set two type of scenario, basing on the following assumptions about certain variables of the model. The basic hypothesis is a constant decreasing of 2% for every variable changed, except every 6 years (a totally arbitrary choice), simulating an increasing amplification of this cycle (every 6 years, the percentage variation of the cost respect to the previous value is double than it and then is multiplied for the number of the occurring, so the first time at year 6, this value is roughly 4, namely 2% multiplied by 2 and then multiplied per variation 1).

**Table 1** describes the hypothesis used in this first step of the analysis.

#### **3. Results**

**Figure 1** shows the results obtained by performing a SVR about the data from IEA [1] for the first scenario considered (**Figure 2**).

The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:


#### **Figure 1.**

*Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).*

*Machine Learning in Estimating CO2 Emissions from Electricity Generation DOI: http://dx.doi.org/10.5772/intechopen.97452*

**Figure 2.**

*Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).*

with a clear improvement of performance of the SVM after tuning. The linear model since the strong relationships between the fuel cost and the LCOE is clearly preferable respect to the SVM (**Figures 1**–**4**).

The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:


Recalling that in the second case the O&M cost was used as a predictor, we can more appreciate the gain in terms of RMSE obtained by using the SVM.

The increasing accuracy of the SVR respect to the LM, can be used to perform a CO2 emission estimation in a cost-effectiveness analysis.

#### **Figure 3.**

*Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.*

**Figure 4.**

*Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X=O&M Cost.*

Let us look at a simple and plain experiment based on IEA data [2] for Italy, 2020 in the following scenario:


In scenario 3 we made a simulation basing on the hypothesis of a sudden shock for the three variables above reported in the 15th year, immediately followed by a linear decrease of them until end of the lifetime, starting from IEA 2020 data as a baseline value.

For scenario 3 the errors in predicting LCOE using O&M Cost over the considered time horizon are:


In Cost-Effectiveness Analysis it is possible to calculate the Incremental Cost-Effectiveness Ratio (ICER), used as a measure of cost the LCOE and used as a measure of effectiveness through the quantity of CO2 emitted. The ICER can be used as a selection criterion between different options then, the winning options will be producing a certain level of emissions.

Now, let us imagine comparing two types of plants of the same technological family, in this case the CCGT. In this hypothetical exercise, the second type of plant is characterized by higher operating costs (+5% of the IEA base value).

In addition to this, let us imagine that the second type of plant has an average load factor of 94%.

*Machine Learning in Estimating CO2 Emissions from Electricity Generation DOI: http://dx.doi.org/10.5772/intechopen.97452*

#### **Figure 5.**

*CO2 emissions from different kind of CCGT plants in scenario 3 (sources: IEA, 2020 + imaginary data).*

Now, let us repeat the simulation performed for scenario 3 for the first type of CCGT plant (the real one), but only from the 20th year.

The meaning of this operation is as follows:


The results are shown in **Figure 5**.

**Figure 5** illustrates what happens using the ICER criterion as a selector of the winning generation option. For the first 20 years, the first type of installation is selected, and the corresponding emissions are those of the blue line. From 20 years of age onwards, using the ICER as a criterion means choosing the second type of plant and the curve that shows the new profile of the emissions is the orange one.

## **4. Conclusions**

ML can help in providing accurate forecasts of CO2 emissions from power generation, especially when we face simultaneous variation of major driver (like

<sup>6</sup> Namely, ICER max/min = ICER +/ ICER\*MAPE.

fuel cost, operating cost of the plant and so on); only a little piece of the possible comparisons between traditional techniques and a particular ML method was shown, focusing on the better performance of the ML one (SVM) respect to the traditional one (the LM).

In our case, the performed step was:


5.defining the trend of the CO2 emissions in the lifetime of the plant by step 4.

Recalling that a basic LCOE model can be brought to a great level of granularity, it is easy to imagine how this type of analysis could gain in depth and significance if the required data are available. Indeed, also in case of missing data, significant simulation can be provided by using each available piece of information on energy costs.

The experiment performed was conducted at the highest level of simplicity to better focus on the reasons that suggest ML integration not only about the engineering features of electricity generation field but also in support decision tools about energy policy.
