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The challenge of Big data is fundamentally concerned with performing data analytics for large amount of heterogeneous data. This data can be collected from different and/or uncorrelated sources. Due to the complexity of such technology; there are still various possible applications and integrations under study particularly in the fields of smart sy...
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This paper constructs an abstract symbol integration system based on big data applied in advertising design. Firstly, the abstract symbols are regarded as high-dimensional data, and the abstract symbols are collected using the local differential privacy algorithm in the big data algorithm. Secondly, the collected data are clustered, which is the pr...
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BioScience is an advanced Python library designed to satisfy the growing data analysis needs in the field of bioinformatics by leveraging High-Performance Computing (HPC). This library encompasses a vast multitude of functionalities, from loading specialized gene expression datasets (microarrays, RNA-Seq, etc.) to preprocessing techniques and data mining algorithms suitable for this type of datasets. BioScience is distinguished by its capacity to manage large amounts of biological data, providing users with efficient and scalable tools for the analysis of genomic and transcriptomic data through the use of parallel architectures for clusters composed of CPUs and GPUs.
Nowadays, energy transition is an interesting topic due to the multi-actor nature of energy management and the conflicts faced by policymakers. Despite the significance of cross-sector partnerships (CSPs) in energy transition, some researchers have evaluated the strategic participation of organizations, primarily through qualitative approaches. Also, the need for social entrepreneurship (SE) to promote the connection between the public and private sectors and civil society is increasing. Moreover, empirical research in non-Western countries is scarce. So, this paper investigates the role of SE in energy saving CSPs in Iran using the mixed method. Initially, we searched and collected documents related to electrical energy savings from various sources. Subsequently, we analyzed the documents to identify the actors and utilized the social network analysis (SNA) approach for analyzing CSP networks. Then, we interviewed experts to identify strategies for enhancing CSPs. The results indicated that the network cohesion value is 0.167, which means a low level of partnership among actors in Iran's electrical energy-saving field. Furthermore, findings showed that public sector organizations dominate Iranian electricity saving partnerships, whereas private and civil sector partnerships should be improved. The comparison of CSP networks showed that SE interventions and enhancing strategies, including new partnerships, increased civil society and private sector effectiveness in developing network cohesion by 10 %. These findings provide important insights that SE can be considered an effective strategy to facilitate energy transition.
The numerous obstacles to large-scale integration of renewable energy sources (RESs) and the mitigating steps
that need to be taken to overcome them in smart grid technology implementation are extensively discussed in
this review article. Main focus is given on the control techniques in microgrids, different supporting measures
such as electric vehicles (EVs), energy storage systems (ESSs), and the monitoring techniques of Microgrid
considering large scale renewable energy integration. The absence of physical factors like reactive power and
frequency makes the DC microgrid less diffcult than the AC microgrid. A comparison of the characteristics of
centralized, decentralized, and distributed control arrangements reveals that the microgrid central controller
(MGCC) bears the majority of the computational load and the cost of computation in centralized control, whereas
local controllers (LCs) bear the least of the load and the cost of computation in completely decentralized control.
Centralized and decentralized control also have less diffcult implementations than distributed control, which has
more complexity. Due to a number of challenges that are discussed in this article, the EV scenario in Bangladesh
is rather challenging. The transportation sector has chosen vehicle to grid (V2G) approaches over other technologies due to a number of its advantages. In the event that a home energy storage system and an emergency
backup storage system are required, V2G can reduce the cost of EVs and be modifed for the local community. An
autonomous power generation and distribution system is the main emphasis of a smart micro grid in this age, and
internet of things (IoT) is utilized in various applications, such as micro grids, intelligent buildings, and intelligent control devices, for monitoring and tracking crucial data. Based on the topics that are presented in this
article, it clearly demonstrates the state-of-the-art research in the area in question.
The fish processing sector is experiencing increased pressure to reduce its energy consumption and carbon footprint as a response to (a) an increasingly stringent energy regulatory landscape, (b) rising fuel prices, and (c) the incentives to improve social and environmental performance. In this paper, a standalone forecasting computational platform is developed to optimise energy usage and reduce energy costs. This short-term forecasting model is achieved using an artificial neural network (ANN) to predict power and temperature at thirty-minute intervals in two cold rooms of a fish processing plant. The proposed ANN function is optimised by genetic algorithms (GA) with simulated annealing algorithms (SA) to model the relationships between future temperature and power and the system variables affecting them. To assess the accuracy of the proposed method, extensive experiments were conducted using real-world data sets. The results of the experiments indicate that the proposed ANN model performs with higher accuracy than (a) the long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture, (b) peephole-LSTM, and (c) the gated recurrent unit (GRU). This paper finds that using GA & SA algorithms; ANN parameters can be optimised. The RMSE obtained by the ANN compared with the second-ranked method GRU was consequently 16% and 4% less for the predicted temperature and power. The results in one particular site demonstrate energy cost savings in the range of 15%–18% after applying the forecast-optimiser approach. The proposed prediction model is used in a fish processing plant for energy management and is scalable to other sites.
