*4.2. Genetic Algorithm Optimization Results*

In this section, the GA optimization results for 21 July 2017 and 16 February 2018 are presented and analyzed for the weighting coefficient values *w*<sup>1</sup> = *w*<sup>2</sup> = 0.5. For the baseline scenario, a flat tariff at 0.07 €/kWh is used. The optimized scenario is calculated taking into account a 2-zone tariff pricing scheme of 0.0675 €/kWh from 8 a.m. to 6 p.m., and 0.0525 €/kWh from 6 p.m. to 8 a.m. (Figure 7).

**Figure 7.** Energy pricing profiles used in the baseline and optimised scenarios.

In Figures 8 and 9, the results of the developed GA optimization approach are presented. The charts on the left columns of these figures illustrate the ANN-based power forecast as a baseline scenario. In the same charts, the GA optimized power profiles demonstrate load-shifting solutions. The related costs are depicted in the right columns of the Figures. The baseline costs are calculated based on the flat tariff of Figure 7, while the GA optimized costs are based on the 2-zone tariff of the same figure.

With respect to the Leaf Lab, it is observed in Figure 8 that load shifting occurs from the high-price to low-price hours. This is also reflected, in terms of the cost profile, on the day which accounts for a reduction of 15.77% from €174.97 to €147.37. Likewise, the load in Summa is shifted outside the high price region, with the baseline daily cost of €20.55 being decreased down to €17.80, a relevant reduction of 13.38%. Similarly, the load shifting in Kite Lab occurs from the high tariff zone towards the morning and the evening hours, without a reduction in total power consumption. In this case, the baseline cost is €101.89, and the optimized total cost is €87.40, achieving a reduction percentage of 14.22%.

**9.**GAoptimisationpowerandcostresultsfor the Leaf Lab,theSummaandtheKiteLabduring16February

The analysis of the winter results is displayed in Figure 9. The shift for the Leaf Lab power profile leads to a cost reduction of 10.23% from €48.21 in the baseline scenario down to €43.27.

Load shifting throughout the 24 h occurs in Summa in a way that changes the overall power profile to the early hours of the day. This transition of loads corresponds to 18.59% of costs savings, reflecting also the differences between the flat and the 2-zone tariff pricing scheme.

With respect to the daily power in Kite Lab during the winter, changes between baseline and optimized scenarios appear to take place in a harmonic way from high to low price hours. In this case, a 14.97% cost saving is achieved, since the baseline daily cost is €118.1, compared to the optimized daily cost of €100.42.

In Figure 10, the total power consumption of the 3 buildings is illustrated. In the first case, the high power consumption according to the baseline power is shifted from working hours towards early morning and late evening hours. In terms of cost, the total baseline cost at district level is 293.95 €, and the total optimized cost is €251.30, corresponding to a 14.51% reduction.

With respect to the winter period, the hourly-district level GA-optimized power values for equal weighting coefficients undergo a smooth differentiation to the left of the graph with respect to the baseline. The district-level total baseline cost is €195.27, and the total optimized cost is €167, achieving a reduction percentage of 14.47%.

According to Table 3, regarding the Leaf Lab, the results for each case prove that the optimization is successful, bearing in mind that the baseline cost is €174.90 and the optimized values range from €142.24 to €153.97, a maximum operational costs percentage reduction of 18.67%. For Summa, as for the Leaf Lab, the optimized cost for each pair of weights is lower than the baseline cost of €20, and varies between €17.80 and €17.12. The percentage reduction in this case reaches 14.4% Furthermore, the optimization for the Kite Lab revealed that the GA produces better results compared to the baseline cost of €101.9 for all pairs of weights ranging from €87.62 down to €85.78. The percentage reduction in this case is up to 15.82%. The last column of the table represents the optimized cost for the group of buildings, which is lower than the baseline cost of €293 for all pairs of weighting coefficients varying from €253.91 to €247.89. The maximum percentage reduction in this case is 15.39%.


**Table 3.** Results of the optimization on 21 July 2017 during the summer period.

Table 4 includes the results of optimization for each pair of weighting coefficients at both the building and district levels for the winter period. The results for the Leaf Lab depict the optimized cost for all weights combinations. As shown, in all cases, the optimized cost varies between €43.48 to €42.81, which is lower than the baseline cost of €48 in this case, and accounts for a percentage reduction of up to 10.81%. Moreover, the optimization for the Summa building revealed genetic algorithm solutions with costs from €23.58 to €23.27, a percentage reduction of 16.89% compared to the baseline cost of €28 in this building. Subsequently, in the Kite Lab, the optimized cost is from €102.06 down to €99.85, equal to a percentage reduction of up to 15.38% lower than the baseline cost of €118. The last column represents the optimized cost in the group of buildings during the winter, varying from €168.19 to €166.53, leading to a percentage reduction of 14.6% compared to the baseline cost of €195.


