A reinforcement learning approach for thermostat setpoint preference learning

Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.

Semiparametric Preference Learning

Unlike traditional supervised learning problems,preference learning learns from data available in the form of pairwise preference relations between instances.Existing preference learning methods are either parametric or nonparametric in nature.We propose in this paper a semiparametric preference learning model,abbreviated as SPPL,with the aim of combining the strengths of the parametric and nonparametric approaches.SPPL uses multiple Gaussian processes which are linearly coupled to determine the preference relations between instances.SPPL is more powerful than previous models while keeping the computational complexity low （linear in the number of distinct instances）.We devise an efficient algorithm for model learning.Empirical studies have been conducted on two real-world data sets showing that SPPL outperforms related preference learning methods.

Learning Style Preferences and the Level of L2 Achievement: A Case Study of EFL College Students

This study investigated the relationship between learning styles preferences of English as foreign language (EFL)college students from three different achievement levels and to explore whether there is statistically significantdifference between different achievement levels and different learning styles. A total of 120 EFL freshman collegestudents from high, intermediate, and basic levels in Taiwan participated in the present study. Using a learning stylepreference checklist, students’ perceptional learning styles were first explored in terms of preferences. 120participants with different levels were classified based on their English scores on College Entrance Exam. With theuse of descriptive statistics and a one-way analysis of variance (ANOVA), the results indicated that specificlearning style preference correlated with certain achievement levels of students. Students with differentachievement levels preferred significantly certain style preference to other style comparing to other achievementlevels. It seems that learning styles preferences may not definitely relate to a student’s achievement levels. Certainvariables may also probably affect learning style preferences with respect to English performance.