Weather forecasting based on data-driven and physics-informed reservoir computing models

Mammedov, Yslam D.; Olugu, Ezutah Udoncy; Farah, Guleid A.

doi:10.1007/s11356-021-17668-z

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…Reservoir computing is also considered to be a promising method for understanding and predicting natural phenomena, such as weather forecasting, earthquake prediction, and ecosystem modeling [12,13]. In this section, we conducted an experiment to test the proposed architecture's efficacy, in this arena.…”

Section: Experimentation For Temperature Predictionmentioning

confidence: 99%

Integrated architecture of ITCAM and reservoir computing for time series data

Kunito,

Akiyama,

Suzuki

et al. 2024

NOLTA

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This paper presents a new architecture to give personality to robots and artificial intelligence (AI). Communication robots are used for elderly care and as AI assistants. However, the currently available communication robots can only perform general actions and have limitations in following instructions. In this study, we developed a new architecture that combines Ternary Content Addressable Memory with Individuality (ITCAM) and reservoir computing, which reflects chip variations in search results. This architecture is used to perform learning and inference by using the weights of nine field-programmable gate array chips of the same standard. In experiments using sine waves and triangular waves, the individuality range of each chip performed within a 5% range. This architecture allows individual chips to vary their predictions while referring to teaching data. Furthermore, experiments using atmospheric temperature show the promise that an ITCAM-based Reservoir Computing Architecture (IRC) approach can be used to predict the actual conditions in the volatile real world. Even in years where the normalized average temperature changed significantly including cases with variances of more than 10% per day compared to previous years, the IRC model can generate results within a range of 4%.

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Section: Experimentation For Temperature Predictionmentioning

confidence: 99%

Integrated architecture of ITCAM and reservoir computing for time series data

Kunito,

Akiyama,

Suzuki

et al. 2024

NOLTA

View full text Add to dashboard Cite

show abstract

“…Hu et al proposed a new forecasting approach to estimate the wind power prediction intervals to quantify the prediction uncertainty [326]. Mammedov et al proposed a weather prediction RC method divided into two models for wind speed and atmospheric system forecasting [327]. Ferreira et al introduced what they call the reservoir computing and design training, which simultaneously optimizes reservoir parameters, topology, and weights, avoiding some of the drawbacks of RC implementation, namely spectral radius rescaling [328].…”

Section: Modeling Wind Powermentioning

confidence: 99%

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

et al. 2023

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To face the impact of climate change in all dimensions of our society in the near future, the European Union (EU) has established an ambitious target. Until 2050, the share of renewable power shall increase up to 75% of all power injected into nowadays’ power grids. While being clean and having become significantly cheaper, renewable energy sources (RES) still present an important disadvantage compared to conventional sources. They show strong fluctuations, which introduce significant uncertainties when predicting the global power outcome and confound the causes and mechanisms underlying the phenomena in the grid, such as blackouts, extreme events, and amplitude death. To properly understand the nature of these fluctuations and model them is one of the key challenges in future energy research worldwide. This review collects some of the most important and recent approaches to model and assess the behavior of power grids driven by renewable energy sources. The goal of this survey is to draw a map to facilitate the different stakeholders and power grid researchers to navigate through some of the most recent advances in this field. We present some of the main research questions underlying power grid functioning and monitoring, as well as the main modeling approaches. These models can be classified as AI- or mathematically inspired models and include dynamical systems, Bayesian inference, stochastic differential equations, machine learning methods, deep learning, reinforcement learning, and reservoir computing. The content is aimed at the broad audience potentially interested in this topic, including academic researchers, engineers, public policy, and decision-makers. Additionally, we also provide an overview of the main repositories and open sources of power grid data and related data sets, including wind speed measurements and other geophysical data.

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“…At present, the effectiveness of hybrid modelling method has been verified in chemical engineering, steelmaking process, weather forecast and other fields. [18][19][20][21] However, the application of such method in hot metal pretreatment desulphurisation process is rarely reported at present.…”

Section: Introductionmentioning

confidence: 99%

A novel soft sensing modelling approach for sulphur content estimation in hot metal pretreatment process

Shi,

Lv,

Mei

2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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In the hot metal pretreatment desulphurisation process, the sulphur content needs to be controlled precisely. A novel soft sensing modelling method is proposed to address the problem that sulphur content cannot be directly measured online. Firstly, the main desulphurisation behaviour is represented with the proposed simplified mechanism model for boosting computational efficiency. Moreover, the complex influence of the changing operation condition on the parameters of the simplified mechanism model is described with neural networks to compose a hybrid structured hz. Then, for training this hybrid model, a conjoint learning scheme is designed to ensure stability and reliablity. The experimental results show that the proposed hybrid model obtains higher prediction accuracy and better robustness compared with the pure mechanism modelling technique and the existing benchmark data-driven modelling methods. In addition, the proposed method is able to provide real-time prediction ability, which is substantial to the optimisation control of the hot metal pretreatment process.

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Weather forecasting based on data-driven and physics-informed reservoir computing models

Cited by 8 publications

References 34 publications

Integrated architecture of ITCAM and reservoir computing for time series data

Integrated architecture of ITCAM and reservoir computing for time series data

Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey

A novel soft sensing modelling approach for sulphur content estimation in hot metal pretreatment process

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