Abstract:Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major conc… Show more
“…This paper is part of our broader work on the use of information and communication technology (ICT) to address challenges facing smart cities and societies. Our work on this topic has included the concept of Deep Journalism, 145 , 146 as well as research on topics such as transportation, 146 tourism, 147 smart families and homes, 148 healthcare services for cancer, 58 mental health, 149 education during the COVID-19 pandemic, 150 energy systems 151 and AI-based event detection. 152 Future work will be directed to improving the methodological approach presented in this paper using advanced deep learning methods and their applications to investigate and improve labour economics and other problems facing our societies.…”
The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958–2022 and for the professionals’ perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.
“…This paper is part of our broader work on the use of information and communication technology (ICT) to address challenges facing smart cities and societies. Our work on this topic has included the concept of Deep Journalism, 145 , 146 as well as research on topics such as transportation, 146 tourism, 147 smart families and homes, 148 healthcare services for cancer, 58 mental health, 149 education during the COVID-19 pandemic, 150 energy systems 151 and AI-based event detection. 152 Future work will be directed to improving the methodological approach presented in this paper using advanced deep learning methods and their applications to investigate and improve labour economics and other problems facing our societies.…”
The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958–2022 and for the professionals’ perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.
“…Residential customers as the main users of electrical energy, have a strong influence on electricity demand due to the randomness of their household activities and the flexibility of their appliance usage patterns (Wang et al, 2022). The promotion and application of smart control systems based on energy monitoring, management, and data analysis play a crucial role in optimizing energy utilization and providing personalized recommendations (Alsaigh et al, 2023;Paneru and Tarigan, 2023). The development and diffusion of smart home devices such as smart thermostats, smart lighting and smart appliances support the monitoring and management of energy behavior in the household (Alhussein et al, 2020;Moadab et al, 2021), to better control energy use and improve energy efficiency.…”
Section: Transitioning Towards Smart Energymentioning
Households are an important sector in carrying out human development activities, accounting for more than 30% of the total global energy consumption. The continued growth of household energy consumption (HEC) and carbon emissions is threatening economic and environmental sustainability. This review focuses on the research in the field of HEC and conducts a bibliometric analysis of research articles from the Web of Science Core Collection since 2000. The results show that: 1) HEC research has undergone rapid development since 2014, and interdisciplinary fusion and collaborative research have become dominant trends. 2) Keyword co-occurrence analysis clearly identifies the current urgent themes, including energy demand and its determinants, environmental impact factors and assessments, and energy-saving technologies and emission reduction measures. 3) The analysis of citations reveals that economic models, such as input-output models and life cycle assessment, are frequently employed in the field of HEC. Based on a summary of household energy-saving and emissions reduction work, this paper critically discusses the limitations of existing measures such as smart home technology, sustainable energy systems, and behavioral interventions. The main directions for promoting household energy-saving development in the future are identified: including improving the security and customer engagement of smart home technology, focusing on the availability and stability of sustainable energy, and paying more attention to low-income and aging households in behavioral intervention measures. One of the important obstacles facing research is how to reduce energy management efficiency and usage costs through technology and policy.
“…Cocchi et al (2018) demonstrated that machine learning methods have higher prediction accuracy than statistical methods in complex power generation prediction problems. Statistical methods have good interpretability, but machine learning methods are often considered to be "black box problems" (Kane et al, 2014;Alsaigh et al, 2023).…”
In the post-COVID-19 era, countries are paying more attention to the energy transition as well as tackling the increasingly severe climate crisis. Renewable energy has attracted much attention because of its low economic costs and environmental friendliness. However, renewable energy cannot be widely adopted due to its high intermittency and volatility, which threaten the security and stability of power grids and hinder the operation and scheduling of power systems. Therefore, research on renewable power forecasting is important for integrating renewable energy and the power grid and improving operational efficiency. In this mini-review, we compare two kinds of common renewable power forecasting methods: machine learning methods and statistical methods. Then, the advantages and disadvantages of the two methods are discussed from different perspectives. Finally, the current challenges and feasible research directions for renewable energy forecasting are listed.
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