Dynamic energy demand prediction and related control system for UK households

PhD ThesisDomestic energy consumption is not only based on the type of appliances, weather conditions, and house type; it is also highly depended on related occupancy profiles. In order to manage and optimise energy generation and the effective use of energy storage, it is important to be able to accurately predict energy demand in advance. However, high-resolution (like below 1-min) occupancy profiles for domestic UK households are not ideally possible to be recorded or measured in nature. Therefore, an alternative approach to transfer particular electricity load to the number of active occupancy during selected time interval is identified by analysing the average electricity consumption of occupancy in this study. Real load data analysis for three type of participated UK households is presented throughout the year. Then the seasonal synthetic high-resolution (30s) occupancy patterns for each household are generated independently. Weekday occupancy profiles are collected seasonally and used in a Markov-Chain model to produce particular occupancy daily activity sequence for each household. A stochastic model by using Markov-Chain Monte Carlo is presented to randomly generate high-resolution occupancy profiles in dynamic. Then the predicted electricity loads are produced by mapping occupancy profiles to average electricity consumption. By validating the predicted results, it is found that maximum of sub-hourly aggregate result can mostly cover the measured demand in advance. Therefore, it is set the sub-hourly electricity demand boundary independently for each household during weekday throughout the year. Heat demand for each household is simulated in sub-hourly resolution by using DesignBuilder with EnergyPlus throughout the year. Thus, sub-hourly energy demand of each household is applied in the control system of Bio-fuel Micro Trigeneration with Hybrid Electrical Energy Storage. The control system is designed and implemented by using Siemens software STEP-7 S-300 and WinCC. In addition, the predicted energy demands are utilized into the optimization of the control system. The comparison of optimized and general control strategies shows that optimized strategies by applying prescient sub-hourly energy demand can improve system efficiency significantly

Complexation of Z-ligustilide with hydroxypropyl-β-cyclodextrin to improve stability and oral bioavailability

To improve the stability and oral bioavailability of Z-ligustilide (LIG), the inclusion complex of LIG with hydroxypropyl-β-cyclodextrin (HP-β-CD) was prepared by the kneading method and characterized by UV-Vis spectroscopy, differential thermal analysis (DTA) and Fourier transform infrared (FTIR) spectroscopy. LIG is capable of forming an inclusion complex with HP-β-CD and the stoichiometry of the complex was 1:1. Stability of the inclusion complex against temperature and light was greatly enhanced compared to that of free LIG. Further, oral bioavailability of LIG and the inclusion complex in rats were studied and the plasma drug concentration-time curves fitted well with the non-compartment model to estimate the absolute bioavailability, which was 7.5 and 35.9 %, respectively. In conclusion, these results show that LIG/HP-β-CD complexation can be of great use for increasing the stability and biological efficacy of LIG

PEANUT: A Human-AI Collaborative Tool for Annotating Audio-Visual Data

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downstream tasks, such as video retrieval, AR/VR, and accessibility, the performance and adoption of existing audio-visual models have been impeded by the availability of high-quality datasets. Annotating audio-visual datasets is laborious, expensive, and time-consuming. To address this challenge, we designed and developed an efficient audio-visual annotation tool called Peanut. Peanut's human-AI collaborative pipeline separates the multi-modal task into two single-modal tasks, and utilizes state-of-the-art object detection and sound-tagging models to reduce the annotators' effort to process each frame and the number of manually-annotated frames needed. A within-subject user study with 20 participants found that Peanut can significantly accelerate the audio-visual data annotation process while maintaining high annotation accuracy.Comment: 18 pages, published in UIST'2