Seppo Sierla
Biographic Data
| ID | 6458932 |
|---|---|
| NAME | Seppo Sierla |
| GIVEN NAMES | Seppo |
| FAMILY NAME | Sierla |
| SIGNATURE | SIERLA S |
| AFFILIATIONS | Aalto University |
| ORCID | 0000-0002-0402-315X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Deep reinforcement learning for fuel cost optimization in district heating
This study delves into the application of deep reinforcement learning (DRL) frameworks for optimizing setpoints in district heating systems, which experience hourly fluctuations in air temperature, customer demand, and fuel prices. The potential for energy conservation and cost reduction through setpoint optimization, involving adjustments to supply temperature and thermal energy storage utilization, is significant. However, the inherent nonlinea…
An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building…
No prominent works on this page.
An overview of machine learning applications for smart buildings
The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building…
Deep reinforcement learning for fuel cost optimization in district heating
This study delves into the application of deep reinforcement learning (DRL) frameworks for optimizing setpoints in district heating systems, which experience hourly fluctuations in air temperature, customer demand, and fuel prices. The potential for energy conservation and cost reduction through setpoint optimization, involving adjustments to supply temperature and thermal energy storage utilization, is significant. However, the inherent nonlinea…
Artificial Intelligence (2 works) · Building Energy and Comfort Optimization (2 works) · Computer Science (2 works) · Engineering (2 works) · Smart Grid Energy Management (2 works) · Adaptability (1 works) · Air conditioning (1 works) · Air Quality Monitoring and Forecasting (1 works) · Architectural engineering (1 works) · Building automation (1 works)