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ABSTRACT: The problem of order-preserving matching has gained attention lately. The text and the pattern consist of numbers. The task is to find all the substrings in the text which have the same length and relative order as the pattern. The problem has applications in analysis of time series. We present a new sublinear solution based on filtration. Any algorithm for exact string matching can be used as a filtering method. If the filtration algorithm is sublinear, the total method is sublinear on average. We show by practical experiments that the new solution is more efficient than earlier algorithms.
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ABSTRACT: A combined heat and power (CHP) optimization model with heat storage is proposed to minimize the production cost and to maximize the revenue from power sales based on a sliding time window method. The model can be applied both for operating heat storage optimally and supporting investment planning for a new storage. Heat demand is forecasted based on a weather forecast. Each day the heat demand and power price forecasts are input to a generic CHP optimization model for a several-day time window to obtain a heat storage operation plan. Then only the first day of the plan is implemented with actual power price and heat demand using a single-day optimization model to compute the actual production amount, fuel costs and revenue from power sales. After that, the time window is slid one day forward, and the above-mentioned process is repeated. In the test runs, forecasts for power price and temperature are simulated by disturbing actual (historical) data by the Wiener process (random walk). To evaluate the benefit and validate the proposed method, the results are compared with the no-storage case and the theoretical optimum assuming perfect demand and price forecasts. The results show that the revenue from power sales can be significantly improved. The method is used to evaluate the benefit of different sized storages for the CHP system. Also the effect of the width of the time windows on the performance of the method is evaluated. The model was tested using real-life heat demand data for the city of Espoo in Finland, and NordPool spot market data for power price for a one year time horizon. The results indicate that considering the forecasting uncertainty, 5-day sliding time window method can obtain 90% of the theoretically possible cost savings that can be derived based on perfect forecasts.
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ABSTRACT: Over the past few years, shared spaces for students, entrepreneurs and faculty have become popular on university campuses. This study aims at increasing understanding on how a new co-working space is developed on campus, and what the different stakeholders’ roles are in the development process. The single case study is a recently emerged ‘learning, networking, and innovation platform’ for energy, named the Energy Garage. The Energy Garage is available to all university students, faculty, and businesses with an interest in energy related topics. Using archived material and interviews with key stakeholders, the study analyses the development path of the Energy Garage, placing special focus on the role of students during the different phases. The study finds that, while the initiative for Energy Garage came from faculty, students have successfully been given a major role in the planning and operational management of the space. The findings provide an insight into other similar initiatives, which continue to gain popularity on university campuses.
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