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The literature that a lack of integration between the performance shaping factors (PSFs) and the energy management performance (EMP) is one of the critical problems that prevent performance improvement and reduces the power plant's efficiency. To solve this problem, this article aims to achieve two main objectives: (1) Systematically investigate and identify the critical success factors (CSFs) for integration with PSFs and EMP; (2) Develop a novel modelling approach to predict the performance of power plants based on innovative integrated strategies. The research methodology is grounded on the theoretical and practical approach to improving performance. The Newcastle Ottawa Scale (NOS) was used to assess the quality of the literature that met the criteria. To ensure the reliability and accuracy of the proposed model, the researchers developed a hypothesis and evaluated the CSFs via a case study in the Iraqi power plants. The findings of this study succeeded in developing a novel modeling approach to predict the performance by integrating the CSFs of both the PSFs and EMP to increase the positive interaction and energy efficiency of power plants. The results confirmed the validity of the selected hypotheses and verified the positive and important relationship with the success and improvement of the performance in power plants. However, the lack of consistency and balance in the current studies indicates that the performance strategy in power plants did not receive sufficient attention and needs further investigations. [ABSTRACT FROM AUTHOR]

Industry 4.0 is expected to play a crucial role in improving energy management and personnel performance in power plants. Poor performance problem in maintaining power plants is the result of both human errors, human factors and the poor implementation of automation in energy management. This problem can potentially be solved using artificial intelligence (AI) and an integrated management system (IMS). This article investigates the current challenges to improving personnel and energy management performance in power plants, identifies the critical success factors (CSFs) for an integrated intelligent framework, and develops an intelligent framework that enables power plants to improve performance. The theoretical basis is founded on a systematic literature review to locate 110 out of 3108 papers studied carefully to examine the performance architecture that best enables effective maintenance. The findings from this literature review are combined with expert judgment and the big data advantages of AI applications to develop an intelligent model. Data are collected from a power plant in Iraq. To ensure the reliability of the proposed model, various hypotheses are tested using structural equation modeling. The results confirm that the measurement model is acceptable, and that the hypotheses are supported and significant. A case study demonstrates the strong relationship and significance between big data of performance and the CSFs. It is hoped that this model will be adopted to enable performance improvement in power plants. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has directly impacted the electric power industry; the energy sector has experienced huge losses in electricity production. These losses have also affected the reliability of communication and employees' performance, hence destabilizing the electric power system. This article aims at achieving two objectives. First, analyzing the impact of the COVID-19 pandemic on the communication of performance (human error and human factors) and energymanagement in electricity production. Second, to develop a conceptual framework model to alleviate effects of the pandemic on the power sector and then improve energy management and human performance. This paper involves investigating the influence of the COVID-19 pandemic on the global production of electricity in the first quarters of 2019 and 2020. A conceptual model was developed based on a case study. Additionally, to ensure reliability, a variable, namely COVID-19, was used as a moderator to examine the effects of the independent and dependent variables. The results show that scores for the internet of things (IoT) with awareness and communication (A&C) and workplace environment management were high with Cronbach's alpha value of 0.87 for the IoT and 0.89 for A&C. These numbers are important indicators of factors that could affect performance and energy management and should not be overlooked by the top management. The results also indicate that the pandemic has had a direct effect on the electricity production sector, and the conceptual framework model revealed that COVID-19, as a moderator, has a direct effect on the variables that significantly affect the improvement of both energy management and employee performance. The case study's results confirm the poor performance in power plant maintenance and operation, in which human error would increase especially in Iraqi power plants that have not yet adopted any internationally recognized standards for energy management. This paper contributes to the literature studying COVID-19's impact on the electricity sector in two ways: first, by developing a model to assist the electricity production sector mitigating effects of the COVID-19 pandemic, and second, by providing a detailed investigation into the pandemic's impact on the electricity sector's global production. The findings are hoped to assist researchers and research centers in understanding the general and specific framework to manage the pandemic's effects on electricity production. [ABSTRACT FROM AUTHOR]

(1) Background: this paper aimed at modeling the sustainable integration of quality and energy management system (IQEM) via identifying critical success factors (CSFs) and analyzing the effect on energy management; (2) Methods: The research adopted theoretical and practical methods, through carefully examining the literature to extract the research gap and CSFs that establish a sustainable model for the integration of quality and energy management, while the practical method was energy experts' arbitration and to develop a sustainable model in power plants. The study used SmartPLS and SPSS software for analysis purposes, collected data using a 5-point Likert scale and employed a cross-sectional approach survey questionnaire; (3) Results: The research succeeded in identifying the most important CSFs necessary for the sustainable integration of (IQEM). This investigation discovered that the identified CSFs are significantly related to the electricity sector's energy management integration success (EMIS). The study's results showed that the identified IQEM's CSFs, such as EP with p-values (0.000), SQI (0.000), EMT (0.019), A (0.003), SP (0.010), are significantly associated with EMIS and improve quality and energy management; (4) Conclusions: This study succeeded in modeling a framework that ensures integrated and sustainable success between energy management and quality in developing countries power plants. [ABSTRACT FROM AUTHOR]