Energy Forecasting with Building Characteristics Analysis

With the installation of smart meters, high resolution building-level energy consumption data become increasingly accessible, which not only provides more accurate data for energy forecasting at the aggregated level but also enables datadriven energy forecasting for individual buildings. On the one hand, individual buildings exhibit high randomness, making the forecasting problem at the building-level more challenging. On the other hand, buildings usually have their own characteristics, therefore such valuable information needs to be considered in the forecast models at the aggregation level. In this paper we investigate how unique characteristics of buildings could affect the performance of forecasting models and aim to identify defining patterns of buildings. The usefulness of the proposed approach is demonstrated using data from three real-world buildings

Model-based optimal control of window openings for thermal comfort

Passive cooling via natural ventilation through window openings is a low-carbon strategy to minimize cooling demand and to adapt to the rising ambient temperatures due to climate change. However, relying on the manual control of windows by occupants is not always optimal for maintaining indoor thermal comfort. In this study, a model-based approach using dynamic thermal simulation program EnergyPlus is used for the optimal control of window openings to maintain indoor thermal comfort. Based on the day-ahead weather forecast, the window opening schedule for the next 24 h is optimized through iteration. Results indicate that the proposed optimal control method significantly improves indoor thermal comfort than using some most commonly used manual control and automated control based on hourly set-point and outdoor temperatures

Review of discrete fracture network characterization for geothermal energy extraction

Geothermal reservoirs are highly anisotropic and heterogeneous, and thus require a variety of structural geology, geomechanical, remote sensing, geophysical and hydraulic techniques to inform Discrete Fracture Network flow models. Following the Paris Agreement on reduction of carbon emissions, such reservoirs have received more attention and new techniques that support Discrete Fracture Network models were developed. A comprehensive review is therefore needed to merge innovative and traditional technical approaches into a coherent framework to enhance the extraction of geothermal energy from the deep subsurface. Traditionally, statistics extracted from structural scanlines and unmanned aerial vehicle surveys on analogues represent optimum ways to constrain the length of joints, bedding planes, and faults, thereby generating a model of the network of fractures. Combining borehole images with seismic attributes has also proven to be an excellent approach that supports the stochastic generation of Discrete Fracture Network models by detecting the orientation, density, and dominant trends of the fractures in the reservoirs. However, to move forward to flow modelling, computation of transmissivities from pumping tests, and the determination of hydraulically active fractures allow the computation of the hydraulic aperture in permeable sedimentary rocks. The latter parameter is fundamental to simulating flow in a network of discrete fractures. The mechanical aperture can also be estimated based on the characterization of geomechanical parameters (Poisson’s ratio, and Young’s modulus) in Hot Dry Rocks of igneous metamorphic origin. Compared with previous review studies, this paper will be the first to describe all the geological and hydro-geophysical techniques that inform Discrete Fracture Network development in geothermal frameworks. We therefore envisage that this paper represents a useful and holistic guide for future projects on preparing DFN models

NOVEL AMINO ACID TRANSPORTER-TARGETED RADIOTRACERS FOR BREAST CANCER IMAGING

Breast cancer is the most common malignancy among women in the world. Its 5-year survival rate ranges from 23.4% in patients with stage IV to 98% in stage I disease, highlighting the importance of early detection and diagnosis. 18F-2-Fluoro-2-deoxy-glucose (18F-FDG), using positron emission tomography (PET), is the most common functional imaging tool for breast cancer diagnosis currently. Unfortunately, 18F-FDG-PET has several limitations such as poorly differentiating tumor tissues from inflammatory and normal brain tissues. Therefore, 18F-labeled amino acid-based radiotracers have been reported as an alternative, which is based on the fact that tumor cells uptake and consume more amino acids to sustain their uncontrolled growth. Among those radiotracers, 18F-labeled tyrosine and its derivatives have shown high tumor uptake and great ability to differentiate tumor tissue from inflammatory sites in brain tumors and squamous cell carcinoma. They enter the tumor cells via L-type amino acid transporters (LAT), which were reported to be highly expressed in many cancer cell lines and correlate positively with tumor growth. Nevertheless, the low radiosynthesis yield and demand of an on-site cyclotron limit the use of 18F-labeled tyrosine analogues. In this study, four Technetium-99m (99mTc) labeled tyrosine/ AMT (α-methyl tyrosine)-based radiotracers were successfully synthesized and evaluated for their potentials in breast cancer imaging. In order to radiolabel tyrosine and AMT, the chelators N,N’-ethylene-di-L-cysteine (EC) and 1,4,8,11-tetra-azacyclotetradecane (N4 cyclam) were selected to coordinate 99mTc. These chelators have been reported to provide stable chelation ability with 99mTc. By using the chelator technology, the same target ligand could be labeled with different radioisotopes for various imaging modalities for tumor diagnosis, or for internal radionuclide therapy in future. Based on the in vitro and in vivo evaluation using the rat mammary tumor models, 99mTc-EC-AMT is considered as the most suitable radiotracer for breast cancer imaging overall, however, 99mTc-EC-Tyrosine will be more preferred for differential diagnosis of tumor from inflammation

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation

Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and target domains share the same set of classes. As a special case, Open-Set Domain Adaptation (OSDA) assumes there exist additional classes in the target domain but are not present in the source domain. To solve such a domain adaptation problem, our proposed method learns discriminative common subspaces for the source and target domains using a novel Open-Set Locality Preserving Projection (OSLPP) algorithm. The source and target domain data are aligned in the learned common spaces class-wise. To handle the open-set classification problem, our method progressively selects target samples to be pseudo-labelled as known classes, rejects the outliers if they are detected as unknown classes, and leaves the remaining target samples as uncertain. The common subspace learning algorithm OSLPP simultaneously aligns the labelled source data and pseudo-labelled target data from known classes and pushes the rejected target data away from the known classes. The common subspace learning and the pseudo-labelled sample selection/rejection facilitate each other in an iterative learning framework and achieve state-of-the-art performance on four benchmark datasets Office-31, Office-Home, VisDA17 and Syn2Real-O with the average HOS of 87.6%, 67.0%, 76.1% and 65.6% respectively

A Bilevel Game-Theoretic Decision-Making Framework for Strategic Retailers in Both Local and Wholesale Electricity Markets

This paper proposes a bilevel game-theoretic model for multiple strategic retailers participating in both wholesale and local electricity markets while considering customers\u27 switching behaviors. At the upper level, each retailer maximizes its own profit by making optimal pricing decisions in the retail market and bidding decisions in the day-ahead wholesale (DAW) and local power exchange (LPE) markets. The interaction among multiple strategic retailers is formulated using the Bertrand competition model. For the lower level, there are three optimization problems. First, the welfare maximization problem is formulated for customers to model their switching behaviors among different retailers. Second, a market-clearing problem is formulated for the independent system operator (ISO) in the DAW market. Third, a novel LPE market is developed for retailers to facilitate their power balancing. In addition, the bilevel multi-leader multi-follower Stackelberg game forms an equilibrium problem with equilibrium constraints (EPEC) problem, which is solved by the diagonalization algorithm. Numerical results demonstrate the feasibility and effectiveness of the EPEC model and the importance of modeling customers\u27 switching behaviors. We corroborate that incentivizing customers\u27 switching behaviors and increasing the number of retailers facilitates retail competition, which results in reducing strategic retailers\u27 retail prices and profits. Moreover, the relationship between customers\u27 switching behaviors and welfare is reflected by a balance between the electricity purchasing cost (i.e., electricity price) and the electricity consumption level

A Bilevel Game-Theoretic Decision-Making Framework for Strategic Retailers in Both Local and Wholesale Electricity Markets

This paper proposes a bilevel game-theoretic model for multiple strategic retailers participating in both wholesale and local electricity markets while considering customers\u27 switching behaviors. At the upper level, each retailer maximizes its own profit by making optimal pricing decisions in the retail market and bidding decisions in the day-ahead wholesale (DAW) and local power exchange (LPE) markets. The interaction among multiple strategic retailers is formulated using the Bertrand competition model. For the lower level, there are three optimization problems. First, the welfare maximization problem is formulated for customers to model their switching behaviors among different retailers. Second, a market-clearing problem is formulated for the independent system operator (ISO) in the DAW market. Third, a novel LPE market is developed for retailers to facilitate their power balancing. In addition, the bilevel multi-leader multi-follower Stackelberg game forms an equilibrium problem with equilibrium constraints (EPEC) problem, which is solved by the diagonalization algorithm. Numerical results demonstrate the feasibility and effectiveness of the EPEC model and the importance of modeling customers\u27 switching behaviors. We corroborate that incentivizing customers\u27 switching behaviors and increasing the number of retailers facilitates retail competition, which results in reducing strategic retailers\u27 retail prices and profits. Moreover, the relationship between customers\u27 switching behaviors and welfare is reflected by a balance between the electricity purchasing cost (i.e., electricity price) and the electricity consumption level