Sunplanter

Although the cost of solar technology has been reduced by nearly seventy percent in the last ten years, the cost of implementing solar panels for residential and commercial use has remained stagnant. Due to the lack of affordable, easily installable solar panel systems on residential properties, the goal of our Senior Design Project is to design a stand-alone solar tracking structure. Since the system is stand-alone, it can be easily implemented on a wide range of properties in most areas of California. Our design for the support of the solar tracking system is a two-pole structure, with a wide base under each pole to eliminate the need for a deep foundation. An electric gearmotor system will drive the rotational motion of the solar tracking function, due to its high power output and relatively affordable cost. A hydraulic damping and blocking system was incorporated to precisely control rotational motion. After testing the system for both tracking and static operation, an 11.4% increase power production was observed. Sunplanter solar tracking systems have the potential to provide a financially viable investment opportunity for customers while having a positive impact on the environment

An efficient algorithm for learning with semi-bandit feedback

We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss estimation procedure called Geometric Resampling (GR). Contrary to previous solutions, the resulting algorithm can be efficiently implemented for any decision set where efficient offline combinatorial optimization is possible at all. Assuming that the elements of the decision set can be described with d-dimensional binary vectors with at most m non-zero entries, we show that the expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a side result, we also improve the best known regret bounds for FPL in the full information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m) over previous bounds for this algorithm.Comment: submitted to ALT 201