*4.3. Limitations of the Adopted Two-Level Model*

The proposed approach entails some level of abstraction with respect to the load shift which is achievable within the capacity of individual systems and components. Evaluating load shift in conjunction with a pricing scheme requires deep knowledge, and depends on the specificities of each case study. In this respect, load shift is determined by technical factors, i.e., installed systems technical characteristics, control scheme, etc., as well as organisational factors, i.e., the potential shift of the industrial operations within each building. A detailed knowledge of the operation of each system in a building, along with data, i.e., power consumption profile, is not available in most cases. This logic can be applied to some extent by using constraints to ensure that a specific percentage of the power at any time remains unchanged. Consequently, optimisation can be conducted based on the flexible share of the consumption power for every hour.

Also, the proposed approach is linked to the accuracy of the prediction, which may vary according to the building under study, and other factors, e.g., type of loads, industrial operations, season, etc. Therefore, it is important to evaluate the risk associated with different prediction error levels according to the examined pricing scheme. Although this risk is low in a two zone pricing scheme, it may become significant when considering dynamic pricing profiles.

## **5. Conclusions**

The main contribution of this work is related to linking ANN short-term electric forecasting and GA multi-objective optimization as a tool for generating and evaluating alternative day-ahead load shifting solutions. The first step of the proposed approach is exploiting Artificial Neural Network modelling for the prediction of the power consumption in a period of 24 h ahead. Predictions of hourly-consumption power levels using day of week, time of day, and external temperature as inputs were obtained for each of the 3 buildings of the Leaf Community (Leaf Lab, Summa, and Kite Lab). The results proved that a close correlation between predicted and actual values exists during the studied summer and winter periods, as evaluated based on correlation coefficient R for the whole

period, as well as Mean Bias Error (MBE) and Mean Average Predicted Error (MAPE) for the specific days used in the optimization process.

The second step was to create an optimization function to include energy cost and load shifting using appropriate variables and constraints. The objective function was minimized using a Genetic Algorithm to obtain solutions at individual building and building group levels. Results demonstrated the effectiveness of this approach in considering alternative pricing schemes and load shifting possibilities as a way to examine cost savings. Cost savings of between 10.81% and 18.67% at the building level were associated with significant load shifting solutions obtained by the GA scheme in the considered two-zone ToU pricing scheme. At the district level, cost savings in the range of 13.34% and 15.39% were obtained.

Future steps in this work may involve: (i) extending research activities to include renewable energy generation and storage capabilities, (ii) reforming the GA obtained solutions so as to take into consideration actual loads (base, fixed, flexible), renewable energy production, and storage, and (iii) exploiting the potential for improvements in power predictions using ANN models.

**Author Contributions:** Conceptualization, N.K. and D.K.; methodology, N.K., E.T. and D.K.; software, N.K. and E.T.; validation, N.K., E.T. and D.K.; formal analysis, N.K.; investigation, N.K., D.K. and E.T.; resources, D.K., C.C. and D.I.; data curation, N.K. and E.T.; writing—original draft preparation, N.K. and E.T.; writing—review and editing, N.K., E.T., D.K. and K.K.; visualization, N.K., D.K. and E.T.; supervision, D.K. and K.K.; project administration, D.K. and C.C.; funding acquisition, D.K., N.K. and C.C.

**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 645677.

**Conflicts of Interest:** The authors declare no conflict of interest.
