Methods and Algorithms for Predictive Analytics of Time Series in Energy Consumption
In the face of the climate crisis caused by global warming and large fluctuations in energy costs, urgent and proactive action on energy consumption is needed. States must minimize carbon dioxide emissions and businesses must optimize their energy costs. In this regard, improving energy efficiency p...
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| Format: | Article |
| Language: | Russian |
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The Fund for Promotion of Internet media, IT education, human development «League Internet Media»
2024-03-01
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| Series: | Современные информационные технологии и IT-образование |
| Subjects: | |
| Online Access: | https://sitito.cs.msu.ru/index.php/SITITO/article/view/1062 |
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| Summary: | In the face of the climate crisis caused by global warming and large fluctuations in energy costs, urgent and proactive action on energy consumption is needed. States must minimize carbon dioxide emissions and businesses must optimize their energy costs. In this regard, improving energy efficiency plays a key role in solving the climate crisis and reducing costs for businesses. In most cases, energy efficiency improvements are proving to be the most cost-effective way to combat climate change, simultaneously reducing energy waste, saving money, and enabling affordable expansion of renewable energy. The active digitalization of the energy industry and the adoption of Internet of Things technologies create favorable conditions for the introduction of artificial intelligence in energy management. This paper presents an overview of Artificial Intelligence (AI) technologies and its potential application in energy management using an ice arena as an example. A dataset collected from a real-world facility representing a multidimensional time series was analyzed, and research on the application of deep learning in predictive model-based control was reviewed. Collectively, these studies demonstrated the potential of AI in the field of control theory. Most of them should be viewed as early exploratory work demonstrating the potential of using machine learning algorithms to solve applied problems in energy consumption. |
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| ISSN: | 2411-1473 |