Buildings are immensely energy-demanding and this fact is enhanced by the expectation of even more increment of energy consumption in the future. In order to mitigate this problem, a lowcost, flexible and high-quality Decision-Making Mechanism for supporting the tasks of a Smart Thermostat is proposed. Energy efficiency and thermal comfort are the two primary quantities regarding control performance of a building's HVAC system. Apart from demonstrating a conflicting relationship, they depend not only on the building's dynamics, but also on the surrounding climate and weather, thus rendering the problem of finding a long-term control scheme hard, and of stochastic nature. The introduced mechanism is inspired by Reinforcement Learning techniques and aims at satisfying both occupants' thermal comfort and limiting energy consumption. In contrast to to existing methods, this approach focuses on a plug&play solution, that does not require detailed building models and is applicable to a wide variety of buildings as it learns the dynamics using gathered information from the environment. The proposed control mechanisms were evaluated via a well-known building simulation framework and implemented on ARM-based, low-cost embedded devices.

CPython is the reference implementation of the Python programming language, as well as the most popular one. Tools like machine learning frameworks, web development interfaces and scientific computing libraries have been built on top of it. Meanwhile, singleboard computers are now able to run GNU/Linux distributions. As a result, CPython's influence today is not limited to commodity servers, but also includes edge and mobile devices. We should thus be concerned with the performance of CPython applications. In this spirit, we investigate the impact of dynamic storage allocation on the execution time, memory footprint and energy consumption of CPython programs. Our findings show that (i) CPython's default configuration is optimized for memory footprint, (ii) replacing this configuration can improve performance by up to 1.135x and (iii) application-specific characteristics define which allocator setup performs best at each case. Additionally, we contribute an open-source means of benchmarking the energy consumption of CPython applications, which we implemented for our experiments. Last but not least, by employing a rigorous and reliable statistical analysis technique, we provide strong indicators that our conclusions are platform-independent.

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