Approach to optimize energy production and demand using systems of inequalities and regression modeling
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Núñez Ivonne, Cano Elia Esther, Rovetto Carlos, Cruz Edmanuel, Smolarz Andrzej, Saldana-Barrios Juan Jose |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 244 - 250 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 8th International Engineering, Sciences and Technology Conference |
| Skrócona nazwa konferencji: | 8th IESTEC 2022 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 19 października 2022 do 21 października 2022 |
| Miasto konferencji: | Panama |
| Państwo konferencji: | PANAMA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The prediction of electricity generation and consumption is a tool of great interest within the electricity system, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management because it allows optimization while maintaining a balance between availability, cost, reliability, and efficiency to manage energy flows in real time between production and demand. In this context, in this article, we will study the problem of prediction of both production and demand (consumption) by type of energy for a set of national data, for this, a method is performed with a system of inequalities to mathematically model the process and its respective computational representation through an analysis using a linear regression model to make the prediction in the future. It was concluded with real data that the predictive model has a good performance and can be used as a support tool in the management and control of the Panamanian electrical network. It would be important to evaluate other models for optimization by testing with real and large-scale data to meet the technical requirements for safe and secure power system operations. Finally, we provide further research challenges that contribute to realize truly smart grid systems. |
