The very specific features of the spot prices, especially occurrence of severe jumps, create a spot price risk for retailers who purchase electricity at unregulated highly volatile prices but resell it to consumers at fixed price. Therefore, it is of high im- portance to forecast whether jump is likely to occur during the next hour. However, to the best of our knowledge, such research has not been devoted to the Czech power market yet. Therefore, the aim of this thesis is to forecast the jump occurrence in the Czech day-ahead market. For this purpose we suggest four logit model spec- ifications, each containing various independent variables (for example, electricity demand, outside temperature, lagged price and various dummy variables) where the variable selection is supported by the previous literature and by the characteristic features of the spot prices. Within the in-sample period we compare the suggested models based on the values of pseudo-R squared and Bayesian information criterion. When evaluating the out-of sample performance of suggested models we apply jump prediction accuracy and confidence, but opposed to the previous literature we sug- gest a kind of sensitivity analysis which, to the best of our knowledge, has not be proposed by any other power research. JEL Classification C25, C32, C51,...

<p>Bit-parallel addition can be performed using a number of adder structures with different area and latency. However, the power consumption of different adder structures is not well studied. Further, the effect of pipelining adders to increase the throughput is not well studied. In this thesis four different adders are described, implemented in VHDL and compared after synthesis. The results give a general idea of the time-delay-power tradeoffs between the adder structures. Pipelining is shown to be a good technique for increasing the circuit speed.</p>

This thesis proposes a decision-feedback equalizer (DFE) scheme for blind ADC-based receivers to overcome the challenges introduced by blind sampling. It presents the design, simulation, and implementation of a 5Gb/s speculative DFE for a 2x blind ADC-based receiver. The complete receiver, including the ADC, the DFE, and a 2x blind clock and data recovery (CDR) circuit, is implemented in Fujitsu’s 65-nm CMOS process. Measurements of the fabricated test-chip confirm 5Gb/s data recovery with bit error rate (BER) less than 1e−12 in the presence of a test channel introducing 13.3dB of attenuation at the Nyquist frequency of 2.5GHz. The receiver tolerates 0.24UIpp of high-frequency sinusoidal jitter (SJ) in this case. Without the DFE, the BER exceeds 1e−8 even when no SJ is applied.

This thesis proposes a decision-feedback equalizer (DFE) scheme for blind ADC-based receivers to overcome the challenges introduced by blind sampling. It presents the design, simulation, and implementation of a 5Gb/s speculative DFE for a 2x blind ADC-based receiver. The complete receiver, including the ADC, the DFE, and a 2x blind clock and data recovery (CDR) circuit, is implemented in Fujitsu’s 65-nm CMOS process. Measurements of the fabricated test-chip confirm 5Gb/s data recovery with bit error rate (BER) less than 1e−12 in the presence of a test channel introducing 13.3dB of attenuation at the Nyquist frequency of 2.5GHz. The receiver tolerates 0.24UIpp of high-frequency sinusoidal jitter (SJ) in this case. Without the DFE, the BER exceeds 1e−8 even when no SJ is applied.

Bit-parallel addition can be performed using a number of adder structures with different area and latency. However, the power consumption of different adder structures is not well studied. Further, the effect of pipelining adders to increase the throughput is not well studied. In this thesis four different adders are described, implemented in VHDL and compared after synthesis. The results give a general idea of the time-delay-power tradeoffs between the adder structures. Pipelining is shown to be a good technique for increasing the circuit speed.

Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the University of Texas at Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads. / text

abstract: The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV “energy management” allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016

The subject of this thesis is to assessment of the accuracy of town plans. The analytical work are transferred to available historical cartographic works with Senica and surrounding area. In the theoretical part of the work deals with describing the documents. Furthermore, it describes methods of measurement. The practical part deals with how to detect positional deviations from the selected database points.

No description available.

Financial time-series are not uncommon to research in an academic context. This is possibly not only due to its challenging nature with high levels of noise and non-stationary data, but because of the endless possibilities of features and problem formulations it creates. Consequently, problem formulations range from classification and categorical tasks determining directional movements in the market to regression problems forecasting their actual values. These tasks are investigated with features consisting of data extracted from Twitter feeds to movements from external markets and technical indicators developed by investors. Cryptocurrencies are known for being evermore so volatile and unpredictable, resulting in institutional investors avoiding the market. In contrast, research in academia often applies state-of-the-art machine learning models without the industry’s knowledge of pre-processing. This thesis aims to lessen the gap between industry and academia by presenting a process from feature extraction and selection to forecasting through machine learning. The task involves how well the market movements can be forecasted and the individual features’ role in the predictions for a six-hours ahead regression task. To investigate the problem statement, a set of technical indicators and a feature selection algorithm were implemented. The data was collected from the exchange FTX and consisted of hourly data from Solana, Bitcoin, and Ethereum. Then, the features selected from the feature selection were used to train and evaluate an Autoregressive Integrated Moving Average (ARIMA) model, Prophet, a Long Short-Term Memory (LSTM) and a Transformer on the spread between the spot price and three months futures market for Solana. The features’ relevance was evaluated by calculating their permutation importance. It was found that there are indications of short-term predictability of the market through several forecasting models. Furthermore, the LSTM and ARIMA-GARCH performed best in a scenario of low volatility, while the LSTM outperformed the other models in times of higher volatility. Moreover, the investigations show indications of non-stationary. This phenomenon was not only found in the data as sequence but also in the relations between the features. These results show the importance of feature selection for a time frame relevant to the prediction window. Finally, the data displays a strong mean-reverting behaviour and is therefore relatively well-approximated by a naive walk. / Finansiella tidsserier är inte ovanliga att utforska i ett akademiskt sammanhang. Det beror troligen inte bara på dess utmanande karaktär med höga ljudnivåer och icke-stationära data, utan även till följd av de oändliga möjligheter till inmatning och problemformuleringar som det skapar. Följaktligen sträcker sig problemformuleringarna från klassificering och kategoriska uppgifter som bestämmer riktningsrörelser på marknaden till regressionsproblem som förutsäger deras faktiska värden. Dessa uppgifter undersöks med data extraherad från twitterflöden till rörelser från externa marknader och tekniska indikatorer utvecklade av investerare. Kryptovalutor är kända för att vara volatila och oförutsägbara till sin natur, vilket resulterar i att institutionella investerare undviker marknaden. I kontrast tillämpas forskning inom den akademiska världen ofta med avancerade maskininlärningsmodeller utan branschens typiska förbearbetningsarbete. Detta examensarbete syftar till att minska klyftan mellan industri och akademi genom att presentera en process från dataextraktion och urval till prognoser genom maskininlärning. Arbetet undersöker hur väl marknadsrörelserna kan prognostiseras och de enskilda variablernas roll i förutsägelserna för ett regressionsproblem som prognotiserar en sex timmar fram i tiden. Därmed implementerades en uppsättning tekniska indikatorer tillsammans med en algoritm för variabelanvändning. Datan samlades in från börsen FTX och bestod av timdata från Solana, Bitcoin och Ethereum. Sedan användes variablerna som valts för att träna och utvärdera en Autoregressive Integrated Moving Average (ARIMA)-modell, Prophet, en Long Short-Term Memory (LSTM) och en Transformer på skillnaden mellan spotpriset och tre månaders framtidsmarknad för Solana. Variablernas relevans utvärderades genom att beräkna deras vikt vid permutation. Slutsatsen är att det finns indikationer på kortsiktig förutsägbarhet av marknaden genom flera prognosmodeller. Vidare noterades det att LSTM och ARIMA-GARCH presterade bäst i ett scenario med låg volatilitet, medan LSTM överträffade de andra modellerna i vid högre volatilitet. Utöver detta visar undersökningarna indikationer på icke-stationäritet inte bara för datan i sig, utan också för relationerna mellan variablerna. Detta visar vikten av att välja variabler för en tidsram som är relevant för prediktionsfönstret. Slutligen visar tidsserien ett starkt medelåtergående beteende och är därför relativt väl approximerad av en naiv prediktionsmodell.