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Status evaluation of electric energy metering device (EEMD) based on artificial intelligence technology
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Many countries around the world are planning to reach 100% renewable energy use by 2050. In this context and due to the recent sharp increase in RE utilization in the global energy mix along with its progressive impact on the world energy sector, the evaluation and investigation of its effect on achieving sustainable development goals are not covered sufficiently. Moreover, an assessment of the emerging role of artificial intelligence for renewable energy utilization toward achieving SDGs is conducted. A total of 17 SDGs were divided into three groups, namely, environment, society, and economy, as per the three key pillars of sustainable development. Renewable energy has a positive impact toward achieving 75 targets across all sustainable development goals by using an expert elicitation method-based consensus. However, it may negatively affect the accomplishment of the 27 targets. In addition, artificial intelligence can help renewable energy enable the attainment of 42 out of 169 targets. However, with the current exponential growth of renewable energy share and artificial intelligence development and addressing certain present limitations, this impact may cover additional targets in the future. Nevertheless, recent research foci overlook essential aspects. The exponential growth of renewable energy share and rapid evolution of artificial intelligence need to be accompanied through the requisite regulatory insight and technology regulation to cover additional targets in the future.
One of the challenging conditions in wellbore management is high-pressure, high-temperature wells that apply large expenditures and maintenance costs to petroleum industries. In this regard, drilling fluid rheology and its crucial factors would help engineers to have a fundamental understanding of the most appropriate wellbore management. Moreover, it can help engineers to control the fluid loss phenomenon. This paper aimed to consider different artificial intelligence and machine learning models to predict the drilling fluid density and select the optimum model, which can be extended to field applications. These models are ANFIS (adaptive neuro-fuzzy inference system), PSO-ANFIS (particle swarm optimization-adaptive neuro-fuzzy inference system), LSSVM-GA (least square support vector machine-genetics algorithm), and RBF (radial basis function) algorithm were modeled in the programming era. In LSSVM-GA model, it is evident that the proper linear equation for the prediction of drilling fluid density is ''y = 1.0041x + 0.0019'' with the correlation factor of (R 2 = 0.9966). Due to the advantages of RBF algorithm to other genetics algorithm, this algorithm was used in this part to predict the drilling fluid density. Therefore, it is evident that the proper linear equation for the prediction of drilling fluid density is ''y = 1.0009x + 0.0034'' with the correlation factor of (R 2 = 0.9999). Consequently, RBF model was selected as the optimum method in drilling fluid prediction. Furthermore, there is a proper match with training and experimental data and the maximum deviation is about-0.006. In ANFIS algorithm, exponential derivation would be preferred to predict the drilling fluid density instead of linear equation. Consequently, the RBF model provide has a good agreement with the experimental drilling fluid density. It is indicated that the model have appropriate accuracy and validity with experimental data.
Transparent energy flows within a factory are the prerequisite for energetic improvements of the involved production machines. With the ongoing digitalization of industrial production, innovative ways of creating energy transparency on the shop floor are emerging. Virtual energy metering points predict the power consumption of a regarded entity and can therefore enable a cost-effective increase in energy transparency on machine level. However, many machines, especially in small and medium-sized enterprises (SMEs), have no external data connection, which prevents the use of data-based energy prediction models. In this paper, a near real-time deployable approach to predict the current energy consumption of production machines without a programmable logic controller (PLC) data connection is presented. By using a Raspberry Pi as low-cost edge analytics device, its integrated camera films the optical signals from light-emitting diodes (LEDs) of different PLC modules, which display the switching state signals of various machine sub-units. In a next step, the filmed PLC information is translated into state signals, which are correlated with temporarily measured electric energy data of the production machine as well as its principal sub-units. After an automated model training and hyperparameter optimization process, the empirical black box model is deployed in a near real-time environment on the Raspberry Pi. Thus, a hybrid virtual energy and resource flow metering point of the production machine as well as its sub-units is generated. In addition, challenges like model training for predicting different production processes as well as the necessary data set size for VMP model generation are addressed. The approach is tested and validated for a metal cutting machine tool and a cleaning machine of the ETA Research Factory at the Technical University of Darmstadt.
Nearly a billion new energy consumers join the society in 13–15 years; and the growing demand for higher standards of living makes the worldwide energy consumption continuously growing. Strategic solutions are therefore required not only for addressing energy gaps but also for eliminating the undesirable environmental effects of energy supply chain to ensure quality and sustainable living. This article advocates leveraging artificial intelligence associated digital technologies to increase energy efficiency, to facilitate carbon trading, and to realize the circular economy vision of countries to mitigate extreme weather conditions and climate change.
One of the most challenging areas of Future Smart Cities Research is the Smart Energy domain. Critical issues related to optimization, provision of smart customizable networks and sophisticated computational techniques and methods enabled by artificial intelligence and machine learning need further investigation. The renewable energy (RE) is a powerful resource for the future global development in the context of climate change and resources depletion. Artificial intelligence (AI) implies new rules of organizing the activities in order to respond to these new requirements. It is necessary to improve the design of the energy infrastructure, the deployment and production of RE in order to face the multiple challenges that will affect the sector’s growth and resilience.. In this research work we exploit the recent developments on the AI adoption for RE sector in European Union (EU). In this respect, we analysed (i) the efficiency of the transformation processes of the RE within the energy chain from Gross Inland Consumption to Final Energy Consumption, (ii) its implications on the structure of renewable energy by source (solar, wind, biomass etc.), (iii) the labour productivity in RE sector compared to the economy as a whole and its correlation with investments level, (iv) the implication of the adoption of AI for RE towards Future Smart Cities Research. The main contribution of this research is the development of a framework for understanding the contribution of AI in the RE sector in Europe. Another bold contribution of this work is the discussion of the implications for Future Smart Cities Research and future research directions.
From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this article demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called "neuroembryogenesis." This framework shares some of the key behavioral aspects of the biological brain, such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. A proof of concept at the end of this article demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. Our framework is open source, and the code is shared with the scientific community at www.feagi.org.
One of the main problems of the data acquired by the power utilities is the presence of outliers affecting the measurements database throughout the electrical system damaging the distribution scenario analysis. This work proposes a new module to complement the measurements made by the metering systems. A detection technique and three outliers correction techniques were developed, based on fuzzy logic, artificial neural networks and the ARIMA model. The first technique, with a fuzzy approach, develops an inference system based on the variations of the previous 3 measurements to determine the future variation. In the second algorithm developed using ANN, the outliers were corrected using a prediction model with 10 previous samples. The last correction technique was based on the ARIMA model with 96 previous measurements. In order to demonstrate the applicability of the developed methods, a case study was carried out on a substation in a city of Paraiba, a Brazilian state. The three techniques of correction of the outliers presented mean relative error less than 5% for all the test scenarios.