Building Energy Analysis Programs
Building Energy Analysis Computer Programs consist of Loads, Systems, Plant, and Economic sub-programs. They are used by engineers and architects in the design and analysis of energy efficient building envelopes, heating, ventilating, and air conditioning, electrical and other service systems. The historical development of energy analysis programs is presented, the methodologies used in various programs are compared, and areas for future improvements are discussed.
Machine learning paradigms for building energy performance simulations
This research seeks to overcome a technical limitation of building energy performance simulations, the computation time, by using surrogate modeling, a class of supervised machine learning techniques where the output is a performance metric. Though early machine learning methods were introduced decades ago, the convergence of computation power, more data collection, and maturation of methods has led to an explosion in the types of problems machine learning can be applied to. A comparison of several common surrogate modeling techniques found that parametric radial basis functions and Kriging are highly accurate regression techniques for predicting building energy consumption. For a single climate, these regression techniques can predict the total energy consumption to within 2% of a detailed energy simulation, but in a fraction of a second, about five orders of magnitude faster. Integrating a Kriging surrogate model with multi-objective optimization, allowed for finding retrofit recommendations in Lisbon that are cost effective and can reduce the present-day energy consumption of an existing apartment by up to 20%. Similarly, integrating surrogate model with multi-objective optimization can find retrofit options in Boston that can reduce the present-day energy consumption and unmet hours in the future. Combined this body of works strives to add value to existing building energy performance simulation tools as more than just an exercise for code compliance but as a real design tool that can guide decision making. Thesis Supervisor: Leon R. Glicksman Title: Professor of Building Technology and Mechanical Engineering
Electrophysiological evidence that a subset of midbrain dopamine neurons integrate the reward signal induced by electrical stimulation of the posterior mesencephalon
This study was aimed at determining whether midbrain dopamine (DA) neurons are trans-synaptically activated by rewarding electrical stimulation applied near the midline in the posterior mesencephalon (PM), and in the affirmative, whether the increase in firing was proportional to the rewarding effectiveness of the stimulation. Experiments were performed on male Long-Evans rats trained to lever press to obtain 400 ms trains of cathodal rectangular pulses. Following the training period, curves relating the rates of responding to the stimulation frequencies were determined at two current intensities and reward thresholds were calculated for each animal. Each animal was then anesthetized with urethane (1.2 g/kg, i.p.) and firing rate of DA neurons were recorded before, during, and after each of 50 trains (1 train/3 s) of stimulation to the PM using stimulation parameters that either sustained near threshold responding (rewarding), or failed to sustain responding (non-rewarding), in the behavioral tests. A total of 24 DA cells were recorded from 13 behaviorally trained animals, and of these, 17 (71%) responded to rewarding stimulation by an increase in firing, five (21%) were unresponsive and two (8%) were inhibited. In 12 of the 17 cells that were activated, the increase in firing was proportional to the rewarding effectiveness of the stimulation rather than the total strength of the stimulation. These results provide evidence that a subset of midbrain DA neurons are trans-synaptically activated by rewarding PM stimulation and constitute a second, or subsequent, stage of the reward-relevant pathway that integrates the PM reward signal.