ABSTRACT Complex ecosystems, from food webs to our gut microbiota, are essential to human life. Understanding the dynamics of those ecosystems can help us better maintain or control them. Yet, reverse-engineering complex ecosystems (i.e., extracting their dynamic models) directly from measured temporal data has not been very successful so far. Here we propose to close this gap via symbolic regression. We validate our method using both synthetic and real data. We firstly show this method allows reverse engineering two-species ecosystems, inferring both the structure and the parameters of ordinary differential equation models that reveal the mechanisms behind the system dynamics. We find that as the size of the ecosystem increases or the complexity of the inter-species interactions grow, using a dictionary of known functional responses (either previously reported or reverse-engineered from small ecosystems using symbolic regression) opens the door to correctly reverse-engineer large ecosystems.
We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on aggregated meter measurements, a machine learning model is usually trained based on large amount of training data coming from a number of residential homes. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data, while load data coming from different regions or electricity users may exhibit heterogeneous usage patterns. Both practical concerns make training a single, centralized NILM model challenging. In this paper, we propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively. Simulation results on benchmark dataset validate proposed algorithm's performance on efficiently inferring appliance-level consumption for a variety of homes and appliances.
Power systems Unit Commitment (UC) problem determines the generator commitment schedule and dispatch decisions for power networks based on forecasted electricity demand. However, with the increasing penetration of renewables and stochastic demand behaviors, it becomes challenging to solve the large-scale, multi-interval UC problem in an efficient manner. The main objective of this paper is to propose a fast and reliable scheme to eliminate a set of redundant or inactive physical constraints in the high-dimensional, multi-interval, mixed-integer UC problem, while the reduced problem is equivalent to the original full problem in terms of commitment decisions. Our key insights lie on pre-screening the constraints based on the load distribution and considering the physical feasibility regions of multi-interval UC problem. For the multistep UC formulation, we overcome screening conservativeness by utilizing the multi-step ramping relationships, and can reliably screen out more constraints compared to current practice. Extensive simulations on both specific load samples and load regions validate the proposed technique can screen out more than 80% constraints while preserving the feasibility of multi-interval UC problem.
The knowledge graph can link massive fragmented data, transform data into knowledge and provide services. However, there is a lack of related applications and research in the field of ocean engineering. In order to solve the above problems and fill the gap in the application research of related knowledge graphs in the field of ocean engineering, we design and implement knowledge graph based intelligent search system in ocean engineering. We define the ontology of the ocean engineering field and adopt a top-down approach to construct the knowledge graph. First, for processing unstructured data, we use BERT-BiLSTM-CRF for named entity recognition and use R-BERT for relationship extraction. Then, the knowledge graph is stored in the Neo4j graph database. Finally, the intelligent search system uses BERT-CRF and Lexicon Matching to parse the query and provide search services through Cypher sentence generation. In the entity extraction experiments of query parsing, we evaluated the BERT-CRF model performance for 100 to 575 project entities, and the experiments continue to improve as the number of project entities is increasing. Knowledge graph based intelligent search system in ocean engineering (OEIS) has been applied to Jiangsu's comprehensive ocean management and monitoring. With the use of the system, we are able to collect more entity data, which will also effectively improve the performance of our intelligent search system.
The energy system is undergoing a fundamental energy transition by integrating low-carbon distributed 5 energy resources (DERs) in distribution networks to accelerate net zero. The increased DER uptake 6 poses significant challenges in operating energy systems to achieve net zero with high reliability and low 7 cost. In particular, the inherent variability of renewable generation, such as solar photovoltaic systems, 8 brings significant uncertainties to the energy system, causing reliability concerns. DERs also cause power 9 quality issues, such as voltage fluctuations. But the distribution system was not designed to support large Our goal in putting together this research topic is to provide a glance at the wide variety of aspects, 51 spanning from technical modeling and assessment to market-based solutions enabling energy transition 52 towards net zero by integrating DERs. We are also aware that the four papers selected are a small fraction 53 of works focusing on DER integration. We hope that these articles will succeed in attracting readers for 54 further research on this significant research topic of smart grids.
Climate change has raised serious concerns prompting urgent and broad actions that extend current operation techniques. Computational methods and artificial intelligence have already shown promising results in power systems applications, including analysis, forecasting and equipment inspection. Nevertheless, the urgency required in the clean energy shift will substantially increase operation uncertainty, as well as control and planning complexity. Leveraging the capabilities of faster computation, better accuracy and stronger decision-making from cutting-edge computational methods and artificial intelligence can be a promising approach to avoid most impacts in the clean energy transition, while improving system reliability, economics and sustainability. This special issue is focused on inviting original research, reviews and experimental evaluations to promote new computational methods and artificial intelligence applications in low-carbon energy systems. In this special issue, there are a total of 19 original research articles to present the state-of-the-art in energy forecasting, situational awareness, multi-energy system dispatch and power system operation. We would like to thank all participating authors for submitting their works to this special section. We really appreciate the anonymous reviewers' valuable efforts. We are thankful to Prof. David Infield, Prof. Tricoli Peitro and Prof. Panos Moutis, who suggested and supported the creation of this special section and nurtured its initial steps, and to Sophie Robinson, Nageen Matlub and Vinay Kumar Nim, Brianna Cooper, and Bhanuchandar Shanthakumar from IET for their administrative and editorial help. Yishen Wang: Conceptualization; writing—original draft. Fei Zhou: writing-review and editing. Josep M. Guerrero: writing-review and editing. Kyri Baker: investigation. Yize Chen: investigation; writing-review and editing. Hao Wang: investigation; writing-review and editing. Bolun Xu: investigation; writing-review and editing. Qianwen Xu: investigation; writing-review and editing. Hong Zhu: investigation. Utkarsha Agwan: investigation.
Distributed energy resources (DERs) can serve as non-wire alternatives (NWAs) to capacity expansion by managing peak load to avoid or delay traditional expansion projects. However, the value stream derived from using DERs as NWAs is usually not explicitly included in DER planning problems. In this paper, we study a planning problem that co-optimizes investment and operation of DERs and the timing of capacity expansion. By including the timing of capacity expansion as a decision variable, we naturally incorporate NWA value stream of DERs into the planning problem. Furthermore, we show that even though the resulting optimization problem could have millions of variables and is non-convex, an optimal solution can be found by solving a series of smaller linear problems. Finally, we present a NWAs planning problem using real data from the Seattle Campus of the University of Washington.
In the context of charging electric vehicles (EVs), the price-based demand response (PBDR) is becoming increasingly significant for charging load management. Such response usually encourages cost-sensitive customers to adjust their energy demand in response to changes in price for financial incentives. Thus, to model and optimize EV charging, it is important for charging station operator to model the PBDR patterns of EV customers by precisely predicting charging demands given price signals. Then the operator refers to these demands to optimize charging station power allocation policy. The standard pipeline involves offline fitting of a PBDR function based on historical EV charging records, followed by applying estimated EV demands in downstream charging station operation optimization. In this work, we propose a new decision-focused end-to-end framework for PBDR modeling that combines prediction errors and downstream optimization cost errors in the model learning stage. We evaluate the effectiveness of our method on a simulation of charging station operation with synthetic PBDR patterns of EV customers, and experimental results demonstrate that this framework can provide a more reliable prediction model for the ultimate optimization process, leading to more effective optimization solutions in terms of cost savings and charging station operation objectives with only a few training samples.
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