Data Mining - Concepts and Techniques.

Introduction to stochastic programming

A systematic literature review of blockchain-based applications: Current status, classification and open issues

Trends in extreme learning machines

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. It is worth studying on how to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate. In this article, a data enhancement method and a framework are proposed to assist wind power forecasting. The proposed method uses the super-resolution perception technology to first detect the completeness and correctness of historical meteorological and wind power data collected by industrial devices. Then, the detected errors are corrected and the missing data are recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data become complete high-frequency data. Based on the enhanced complete high-frequency data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on public datasets are used to demonstrate the effectiveness of the proposed method and framework. With the proposed method and framework, higher frequency data with more detailed information can be achieved, thereby providing support for accurate wind power prediction that was not possible before.

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Super-resolution perception for wind power forecasting by enhancing historical data

In this paper, thermostatically controlled loads (TCLs) to the provision of fast ancillary services are investigated. In a residential community, a population of TCLs in smart buildings is able to respond to fast regulation signals in a distributed way via sparse communication infrastructure. A two-layer distributed consensus protocol (TLDCP) is proposed with the optimal control gain design based on the linear quadratic regulator method. In the proposed TLDCP, the communication graph is divided into two-layer considering both the local interaction of TCLs in each building and interconnection among buildings. This method will achieve fast, dynamic and distributed power regulation with fair power and comfort sharing among all participate TCLs. With different supervisory control (e.g. fluctuation mitigation and frequency regulation), the TLDCP controlled TCLs can provide various grid ancillary services. The case studies validate the effectiveness of the proposed method.

Distributed Consensus Control of Thermostatically Controlled Loads for Fast Ancillary Services

An Internet of Things Framework for the Credit-Based Peer-to-Peer Energy Sharing Mechanism Under the Distributed Negotiation

Linear-layer-enhanced quantum long short-term memory for carbon price forecasting

The real-time carbon emission estimation of generators helps quantify the carbon emission costs and reduce power system emissions of power generation. Accurate estimation relies on the accuracy of the carbon emission factors (EFs) and power generation measurements. The dynamic carbon emission factor (DEF) of generators was proposed recently as a linear function of the output power. However, there is a significant deviation between the modelled DEFs and the actual measurement EFs, especially when the output power is low. This paper first presents the general definition of the DEF to characterize the emissions and focuses on the unit-level DEF (UDEF). The piecewise non-linear UDEF (P-UDEF) model is then proposed, which can better represent the unit emission characteristics. Then an accurate piecewise linear cost approximation method is proposed considering the segment points and extreme points of both P-UDEF and generation costs function. Last, the system carbon emissions and costs estimation are estimated by combined economic emission dispatch (CEED), and the reduction potential is evaluated. Case studies on an IEEE 30-bus system with piecewise linear cost functions show that the proposed P-UDEF can realize real-time emission and cost estimation as well as reduce the total system emissions by considering the incomplete combustion cost of generating units. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/enc2.12078

Junhua Zhao

Papers

Super-resolution perception for wind power forecasting by enhancing historical data

Distributed Consensus Control of Thermostatically Controlled Loads for Fast Ancillary Services

An Internet of Things Framework for the Credit-Based Peer-to-Peer Energy Sharing Mechanism Under the Distributed Negotiation

Linear-layer-enhanced quantum long short-term memory for carbon price forecasting

Real‐time emission and cost estimation based on unit‐level dynamic carbon emission factor