Cost-efficient decarbonization of local energy systems by whole-system based design optimization

On the way toward Net Zero 2050, the UK government set the 2035 target by slashing 78 % emissions compared to the 1990-level. To help understand how an electrified local energy system could contribute to this target and the associated cost, we develop a whole-system based local energy optimization (LEO) model. The model captures a series of state-of-the-art technologies including building fabric retrofit, battery storage, electro-mobility, electro-heating, demand response, distributed renewable, and Peer-to-Peer (P2P) energy trading. And the model enables trade-off assessment between cost and emissions minimization, compares two system operating modes, i.e., cost-oriented and grid-impact-oriented, and evaluates the impacts from weather risks and capital cost assumptions. A case study in Wales reveals (1) capital cost assumptions can lead up to 30.8 % overall cost difference of the local energy system; (2) operating the system in cost-oriented mode can save up to 5 % cost than in the grid-impact-oriented mode; (3) electro-heating by heat pumps has the highest priority among all investigated technologies. Overall, this study demonstrates how to design and operate a cost-efficient and electrified UK local energy system by the whole-system incorporation of near-term technical and business model advances towards a decarbonized future

A Hybrid Failure Diagnosis and Prediction using Natural Language-based Process Map and Rule-based Expert System

Preventive maintenance is required in large scale industries to facilitate highly efficient performance. The efficiency of production can be maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually in which case, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent, or any error in diagnosis is made by an unskilled expert. In many cases, an alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this paper, we designed and developed an automated preventive maintenance system using experts’ experience in detecting failure, determining the cause, and predicting future system failure. There are two main functions in order to acquire and analyze domain expertise. First, we proposed the network-based process map that can extract the expert’s knowledge of the written failure report. Secondly, we designed and implemented an incremental learning rule-based expert system with alarm data and failure case. The evaluation results shows that the combination of two main functions works better than another failure diagnosis and prediction frameworks

Planning urban energy systems adapting to extreme weather

In the context of increasing urbanization and climate change globally, urban energy systems (UES) planning needs adequate consideration of climate change, particularly to ensure energy supply during extreme weather events (EWE) such as heatwaves, floods, and typhoons. Here we propose a two-layer modeling framework for UES planning considering the impact of EWE. An application of the framework to a typical coastal city of Xiamen, China reveals that deploying energy storage (i.e., pumped hydro and battery) offers significant flexibility to ensure the critical demand is met during typhoon as a typical EWE and avoids over investment in supply technologies. This requires an extra 2.8% total cost on investment and operation of UES for 20 years. Planning energy systems with proper consideration of EWE can ensure robust urban energy services even with increasing penetration of fluctuating renewables, and we offer a flexible and computationally efficient paradigm for UES planning considering the impact of EWE

The optimal design and operation strategy of renewable energy-CCHP coupled system applied in five building objects

Abstract(#br)Combined cooling, heating, and power (CCHP) is an economic and eco-friendly technology to mitigate energy issues with remarkable energy efficiency improvement. This study formulates a mixed integer nonlinear programming (MINLP) model for a combined CCHP system coupled with renewable energy, i.e. RCCHP system, which is applied in five different buildings to evaluate the economic and environmental performance under two optimization modes. Net present value (NPV), internal rate of return (IRR) and dynamic payback period (DPP) are introduced as economic indexes, while CO 2 emission reduction rate (CER) is considered as the environmental indicator to determine the optimal combination, capacity, and operation strategies for energy technologies. Results indicate that a combination of electricity purchased at valley period during night with power generated by the combined heating and power (CHP) unit coupled with wind turbine in peak period during daytime is cost-optimal which also enables higher energy efficiency. Meanwhile, the feed-in tariff as well as the uncoordinated electrical and thermal loads both show a significant impact on real-time operation strategies. Compared with the reference separate production (SP) system, the combined system shows better performance when applied to shopping mall under both optimization modes, e.g., with NPV up to 67.65 and 46.61 million RMB, IRR up to 20.70% and 25.10%, and the minimum DPP is 5.49 and 4.82 years under NPV and IRR maximization, respectively

A unified neurocognitive model of semantics language social behaviour and face recognition in semantic dementia

Funder: Chinese Scholarship CouncilFunder: National Key R&D Program of China (2016YFC1306305)Funder: ERC grant (GAP: 670428 - BRAIN2MIND_NEUROCOMP)Abstract: The anterior temporal lobes (ATL) have become a key brain region of interest in cognitive neuroscience founded upon neuropsychological investigations of semantic dementia (SD). The purposes of this investigation are to generate a single unified model that captures the known cognitive-behavioural variations in SD and map these to the patients’ distribution of frontotemporal atrophy. Here we show that the degree of generalised semantic impairment is related to the patients’ total, bilateral ATL atrophy. Verbal production ability is related to total ATL atrophy as well as to the balance of left > right ATL atrophy. Apathy is found to relate positively to the degree of orbitofrontal atrophy. Disinhibition is related to right ATL and orbitofrontal atrophy, and face recognition to right ATL volumes. Rather than positing mutually-exclusive sub-categories, the data-driven model repositions semantics, language, social behaviour and face recognition into a continuous frontotemporal neurocognitive space

A review on modelling methods, tools and service of integrated energy systems in China

An integrated energy system (IES) is responsible for aggregating various energy carriers, such as electricity, gas, heating, and cooling, with a focus on integrating these components to provide an efficient, low-carbon, and reliable energy supply. This paper aims to review the modeling methods, tools, and service modes of IES in China to evaluate opportunities for improving current practices. The models reviewed in this paper are classified as demand forecasting or energy system optimization models based on their modeling progress. Additionally, the main components involved in the IES modeling process are presented, and typical domestic tools utilized in the modeling processes are discussed. Finally, based on a review of several demonstration projects of IES, future development directions of IES are summarized as the integration of data-driven and engineering models, improvements in policies and mechanisms, the establishment of regional energy management centers, and the promotion of new energy equipment

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

BackgroundOnline medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users’ questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users’ information needs. ObjectiveMost online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users’ needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users’ needs in online medical and health communities so that users can perform more targeted information retrieval. MethodsUsing the Chinese online medical and health community “Qiuyi” as an example, we crawled questions posted by users in the “Cardiovascular Disease” section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users’ information needs label as the second-level label. Finally, by constructing a GCN model, users’ questions were automatically classified, thus realizing the multilevel classification of users’ needs. ResultsBased on the empirical research of questions posted by users in the “Cardiovascular Disease” section of Qiuyi, the hierarchical classification of users’ questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method naïve Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users’ needs, which in comparison with the multilevel classification model exhibited a great improvement. ConclusionsA multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users’ information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications

The neuropsychological profiles and semantic-critical regions of right semantic dementia

Introduction: Previous literature has revealed that the anterior temporal lobe (ATL) is the semantic hub of left-sided or mixed semantic dementia (SD), whilst the semantic hub of right-sided SD has not been examined. Methods: Seventeen patients with right-sided SD, 18 patients with left-sided SD and 20 normal controls (NC) underwent neuropsychological assessments and magnetic resonance imaging scans. We investigated the relationship between the degree of cerebral atrophy in the whole brain and the severity of semantic deficits in left and right-sided SD samples, respectively. Results: We found the semantic deficits of right-sided SD patients were related to bilateral fusiform gyri and left temporal pole, whilst the left fusiform gyrus correlated with the semantic performance of left-sided SD patients. Moreover, all the findings couldn't be accounted for by total gray matter volume (GMV) or general cognitive degradation of patients. Discussion: These results provide novel evidence for the current semantic theory, that the important regions for semantic processing include both anterior and posterior temporal lobes. Keywords: Semantic dementia, Lesion-behavior mapping, Laterality of brain atrophy, Semantic deficit

Is China ready for a hydrogen economy? Feasibility analysis of hydrogen energy in the Chinese transportation sector

Hydrogen plays a crucial role in achieving deep decarbonization in the global transportation sector. This study aims to identify an efficient development strategy for hydrogen energy in China's transportation sector. We propose a market acceptance model integrated with life cycle assessment to analyze the feasibility of adopting hydrogen energy in this sector. We develop a profit-fairness optimization model to maximize the profits of the entire industrial chain, ensuring a fair distribution across upstream, midstream, and downstream components. The study finds that hydrogen energy is currently not feasible in China's transportation sector, with consumer preference close to zero. With the anticipated decrease in the cost of future hydrogen applications, achieving suitable development of the industrial chain requires optimal profitability: 39.44 % in production, storage, and delivery industries, and 11.14 % in the hydrogen fuel cell vehicle manufacturing industry in our case. Recommended future marketing strategies include increasing the profitability of production, storage, and delivery industries through guaranteed market-acceptable prices. Additionally, adopting a thin margin strategy for hydrogen fuel cell vehicle manufacturing industries and prioritizing subsidies