In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, market coupling, and global financial instability are contributing to the importance of accurate electricity price forecasting. Since power markets are not all equally developed, different price forecasting methods have been introduced for individual markets. The aim of this research is to introduce a short-term electricity price forecasting method that addresses the problems of price volatility, a varying number of input parameters, varying data availability, and a large number of parameters and input data. Furthermore, the proposed model can be used on any market as it targets the characteristics and specifics of each market. The proposed Hybrid Iterative Reactive Adaptive (HIRA) method consists of two phases. In analysis phase, fundamental parameters which affect the electricity price are identified depending on market development. Obtained parameters are used as data inputs for price forecasting using a hybrid method. The HIRA model combines a statistical approach for large data set analysis and a similar day method with neural network tools. Similar days are examined using a statistical method which combines correlation significance, price volatility, and forecasting accuracy of the historical data. Data are collected based on their availability and electricity prices are forecasted in several iterations. All relevant data for price forecasting are collected, categorized, and arranged using simple indicators which makes the HIRA model adaptive and reactive to new market circumstances. The proposed model is validated using the Hungarian Power Exchange (HUPX) electricity price data records. The results show that with HIRA model forecasting, the error is stable and does not depend on price volatility. The HIRA method has proven to be applicable for forecasting electricity prices in real-time market conditions and enables effective hedging of price risk in the production or market portfolio.
Settling down the software architecture for embedded system is a complex and time consuming task. Specific concerns that are generally issued from implementation details must be captured in the software architecture and assessed to ensure system correctness. The matter is further complicated by the inherent complexity and heterogeneity of the targeted systems, platforms and concerns. In addition, tools capable of conjointly catering for the complete design-verificationdeployment cycle, extra-functional properties and reuse are currently lacking. To address this, we have developed Pride, an integrated development environment for component-based development of embedded systems. Pride is based on an architecture relying on components with well-defined semantics that serve as the central development entity, and as means to support and aggregate various analysis and verification techniques throughout the development -from early specification to synthesis and deployment. Pride also provides generic support for integrating extra-functional properties into architectural definitions.
Dynamic reconfiguration -the ability to hot swap a component or to introduce a new component into a system -is essential to supporting evolutionary change in long-live and highly available systems. A major issue related to this process is to ensure application consistency and performance after reconfiguration. This task is especially challenging for embedded systems which run with limited resources and have specific dependability requirements. We focus on checking resources constraints and propose for a component compliance checking to be performed during its deployment. Our main objective is preserving system integrity during and after reconfiguration by developing a resource efficient dynamic deployment mechanism that will include component validation in respect to available system resources and performance requirements.
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