From Intelligent Energy Management to Value Economy through a Digital Energy Currency: Bahrain City Case Study

Prediction and evaluation of the energy structure under the green finance development in Chongqing municipality, China

Chongqing, as the last ecological barrier of the Upper Yangtze River, is constrained to achieve "dual carbon" goals due to imbalanced energy structure. Based on selecting the energy structure influencing factors through Copula function and Granger causality, a multi-dimensional dynamic support vector machine model (SSA-MFD-SVR-ARIMA) by adopting sparrow algorithm was constructed to predict the proportion of Chongqing's energy structure from 2021 to 2030 under the drive of green finance development, and an optimization path was obtained. The novel findings confirm that (1) the correlated contribution rate of Green Finance to optimizing Chongqing's Energy Structure is 10.8 %; (2) under the sustained growth rate of Green Finance at 4.5 %, the proportion of coal consumption will reach 40.03 % by 2030, and non-fossil energy consumption will account for 27 %. It confirms that Chongqing can achieve the Energy Development Plan assigned by the Central Government in 2025. The research proposes a four-dimensional optimized pathway from a financial perspective that includes green equity investments, digital finance for energy, financing environmental rights and interests, and developing an industry fund. Furthermore, our put forward the safeguard strategies for financing, innovation, linkage, and protection mechanisms of this pathway optimization.

Leveraging Graph Analytics for Energy Efficiency Certificates

As energy efficiency is becoming a subject of utter importance in today’s societies, the European Union and a vast number of organizations have put a lot of focus on it. As a result, huge amounts of data are generated at an unprecedented rate. After thorough analysis and exploration, these data could provide a variety of solutions and optimizations regarding the energy efficiency subject. However, all the potential solutions that could derive from the aforementioned procedures still remain untapped due to the fact that these data are yet fragmented and highly sophisticated. In this paper, we propose an architecture for a Reasoning Engine, a mechanism that provides intelligent querying, insights and search capabilities, by leveraging technologies that will be described below. The proposed architecture has been developed in the context of the H2020 project called MATRYCS. In this paper, the reasons that resulted from the need of efficient ways of querying and analyzing the large amounts of data are firstly explained. Subsequently, several use cases, where related technologies were used to address real-world challenges, are presented. The main focus, however, is put in the detailed presentation of our Reasoning Engine’s implementation steps. Lastly, the outcome of our work is demonstrated, showcasing the derived results and the optimizations that have been implemented.

Blockchain and the Evolving Financial Market: A Literature Review

Blockchain technology has gained widespread utility, particularly in the field of finance due to the popularity of bitcoin. There are other areas such as energy trade and securities market, which have evolved due to blockchain. These fields are new and building regulations and architecture to enhance their functionality. There are studies on blockchain finance and this paper aims to summarize a few of the pathbreaking studies and guide future research in this field. This paper reviews 77 journal publications related to blockchain from 2018 to 2021 available in the Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The selected papers have been grouped into 10 categories. This overview indicates that research in blockchain finance needs to focus on developing better privacy and security mechanism as well as better regulations regarding financial transactions not only to benefit from “Initial Coin Offerings”, but also to diversify investor’s risk.

Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users

The COVID-19 pandemic and the associated lockdown can be regarded as a forced social experiment, the results of which show how to use energy under specific conditions. During this period, there was a reduction in electricity consumption at the level of the power system, but a different specificity distinguishes the group of household users. The article aims at presenting and analysing the identified issues concerning residential electricity users based on the experience from the COVID-19 pandemic lockdown. Data from energy meters from almost 7000 flats in Warsaw’s housing estates during the lockdown in 2020 and the analogous period before the pandemic were used. The analysis showed that, on average, residential users staying practically the whole day in their flats increased their energy consumption, but without increasing their average daily peak power, smoothing the profile in the morning hours to the level reaching the peak power that had occurred in the analogous period before the lockdown. The peak power of the sections feeding the different numbers of dwellings also remained practically unchanged during the lockdown compared to the pre-pandemic period. The pressure to work and educate remotely should contribute to an increase in the digital competence of society, which may result in an increased interest in new forms of activity and cooperation based on demand-side response and prosumption mechanisms, with digital settlements for energy exchange and services.

New Market Model with Social and Commercial Tiers for Improved Prosumer Trading in Microgrids

In the deregulated electricity markets, trading prices are determined by the offer-demand mechanism, and retail consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are determined by the prices of transactions performed on the wholesale market. In parallel with becoming eligible for participating in the market, the consumers use increasingly local generation sources based mostly on renewable electricity generation equipment such as Photovoltaic (PV) panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. This paper proposes a two-tier local market model oriented for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as smart grids, crowdsourcing and energy poverty. Its goals are to improve the possibilities of local prosumers to sell electricity to local consumers and to increase their profitability, compared to the trading model often used in developing markets, of selling the surplus back to the grid via aggregators. The research aims to contribute to the sustainable development of the electricity sector using new and renewable sources of energy, state-of the art technologies and smart contracts, leading to prosumer proliferation and electricity cost reduction for consumers.

Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System

This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5 kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.