How can machine learning be used in smart energy systems?5 answersMachine learning can be used in smart energy systems to improve energy efficiency, optimize grid management, and integrate renewable energy resources. It can be applied in various facets of the energy industry, including power generation, distribution, and consumption. Machine learning techniques such as ARIMA and Bi-LSTM models have been used to predict solar power production, with the Bi-LSTM model outperforming the ARIMA model in terms of accuracy. Machine learning can also be utilized in building energy management systems to forecast energy usage and offer suggestions for reducing energy wastage. In the context of smart grids, machine learning can enhance the security and sustainability of the power system, with various machine learning algorithms being applicable in different aspects of the smart grid. Additionally, deep learning-based techniques have been explored for predicting energy consumption in smart residential buildings, providing optimal models for estimation of prediction performance and uncertainty.
Cybersecurity Modeling and Simulation for Energy Systems: A Survey?5 answersCybersecurity modeling and simulation for energy systems is an important area of research. Several papers have discussed the need for advanced control architectures to enhance the cyber resiliency of future power systems with distributed energy resources. The creation of cyber-physical models for the grid is essential to understand the impact of grid-edge devices and enable increased automation and grid edge intelligence. The IEC 61850-7-420 standard has been developed to include data models for distributed energy resources, but its adoption has been limited due to cybersecurity concerns. Cyber-physical systems, including cyber-physical energy systems, are vulnerable to malicious attacks, and the security of these systems can be enhanced through modeling, simulation, and risk assessment. Overall, there is a need for comprehensive surveys and evaluations of cybersecurity modeling and simulation techniques for energy systems to ensure the resilience and security of these critical infrastructures.
How can simulation programs be used to improve energy efficiency?4 answersSimulation programs can be used to improve energy efficiency by predicting energy consumption, assessing building performance, and identifying energy improvement measures. These programs allow for the estimation of energy savings from different energy conservation measures and the analysis of building designs to achieve major changes in energy consumption at a low cost. They also help in making effective retrofit decisions based on energy demand, resource use, throughput, and overhead costs in manufacturing environments. Simulation programs can be used to examine the effects of occupancy on a building's energy consumption and test occupancy-based HVAC control strategies, contributing to demand-driven control and improved energy efficiency in buildings. By simulating different scenarios and considering various factors, simulation programs provide valuable insights for optimizing energy efficiency in buildings and manufacturing facilities.
Simulation program on energy consumption?5 answersSimulation programs for energy consumption have been developed to understand and assess the levels of energy used by devices. These programs utilize various techniques and methodologies to create energy profiles, differentiate between energy consumed by the simulation engine and the application code, and estimate unrealized energy profile values. The simulation models generated through these programs allow for the investigation of different scenarios and parameters to understand the impact on energy consumption in manufacturing systems. Additionally, a load simulator has been designed to generate time series of consumption based on fixed profiles and defined rules. These simulation programs and models provide a visual and integrated environment for energy simulation analysis, enhancing flexibility and maneuverability in energy consumption assessment.
What are the challenges of energy based models in the real world? Tractability, flexibility?3 answersEnergy-based models face several challenges in the real world. One challenge is the need to represent new mitigation options that are not currently included in many models, particularly in the end-use sector. Another challenge is the doubt surrounding whether current models provide sufficient insights on system feasibility, actor behavior, and policy effectiveness. Additionally, there is a need to expand scenario thinking to incorporate a wider range of uncertainty factors and to improve target setting and alignment with broader policy objectives. Finally, there is a need to address the growing complexity of the energy system and to integrate human behavior and social risks and opportunities into the models. These challenges, if not addressed, may reduce the effectiveness of energy modeling in providing decision support for achieving net-zero greenhouse gas emissions.
What are the challenges of energy based models in machine learning? Tractability, flexibility?4 answersEnergy-based models (EBMs) in machine learning face challenges in terms of tractability and flexibility. Training and sampling on high-dimensional datasets remain difficult for EBMs. The optimization of recovery likelihood, which maximizes the conditional distribution of the data given their noisy versions, provides a more tractable objective compared to marginal likelihood. Additionally, the estimation method used in EBMs is theoretically consistent and learns the correct conditional and marginal distributions at each noise level. EBMs offer a more flexible approach to modeling complex distributions, but require partition function estimation. However, recent advancements in learning conditional and unconditional EBMs for discrete structured data have shown promise in achieving a better trade-off between flexibility and tractability.