Historical Power Research Articles

Ultra-short-term wind power prediction method based on FTI-VACA-XGB model

In order to predict wind power quickly and accurately and reduce the negative impact of wind power instability on the grid, this study proposes an ultra-short-term wind power prediction model based on financial technical indicators and parameter optimization algorithms. Firstly, historical wind power time series data calculates the financial technical indicators. Secondly, the Monte Carlo method and rank ant colony algorithm are used to optimize the parameters of financial technical indicators calculation. Finally, the future wind power is predicted based on the XGBoost algorithm combining financial technical indicators with historical power. The proposed model is validated on several wind power datasets from different countries and compared with various comparative models, leading to several important conclusions: (1) Fintech indicators can effectively indicate the intrinsic characteristics of wind power time-series data. (2) The variational ant colony algorithm can make the financial technical indicators better fit the wind power time-series data trends. (3) The proposed model has high prediction accuracy and speed and has similar prediction accuracy to the mainstream deep learning models. (4) The proposed model is not limited by meteorological, geographical, and seasonal factors and can make predictions by relying only on historical wind power data.

What is missing to understand sustainability?

Despite the enormous global problems linked not only to the ecological sphere, but also to the environmental, political, economic, social and cultural dimensions, humanity continues to consider that the discourse of sustainability has a close and unique relationship with nature. It is not like this. This text aims to make a deeper reflection on the historical context, interests and power relations that derive from considering sustainability as an alternative development model. More than a neutral discourse, sustainability must always land in a specific historical and spatial context, which allows it to adapt, and then perhaps become more operational and functional. The hypothesis places the media as simplistic and, at times partial interlocutors; but definitely not neutral, which prevents the individual from understanding the complex dynamics of the planet and the possible solutions to current problems. It is not only the fact that the media define what sustainability is, it is also important to reflect on how and why they do it.

Intersectionality and public understandings of health inequity in England: learning from the views and experiences of young adults

ObjectivesAttempts to reduce health inequities in England frequently prioritise some equity dimensions over others. Intersectionality highlights how different dimensions of inequity interconnect and are underpinned by historic and institutionalised power imbalances. We aimed to explore whether intersectionality could help us shed light on young adults’ understanding of health inequities. Study designThe study incorporatedqualitative thematic analysis of primary data. MethodsOnline focus groups with young adults (n = 25) aged 18–30 living in three English regions (Greater London; South Yorkshire/Midlands; North-East England) between July 2020 and March 2021. Online semistructured interviews (n = 2) and text-based communication was conducted for participants unable to attend online groups. ResultsYoung adults described experiencing discrimination, privilege, and power imbalances driving health inequity and suggested ways to address this. Forms of inequity included cumulative, within group, interacting, and the experience of privilege alongside marginalisation. Young adults described discrimination occurring in settings relevant to social determinants of health and said it adversely affected health and well-being. ConclusionIntersectionality, with its focus on discrimination and identity, can help public health stakeholders engage with young adults on health equity. An upstream approach to improving health equity should consider multiple and intersecting forms of discrimination along with their cultural and institutional drivers.

The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.

Mental Health Literacy and School-Student-Family Alliance: A Brief Narrative Review of Canada's Individualized Education Plan.

This review article addresses the historical contextof power dynamics in individualized education planning processes for exceptional students within Canada's K-12 (i.e., from kindergarten to grade 12) education system. It highlights how such dynamics have created conditions for educators' internalized bias toward students with special needs. This article presents empirical evidence supporting the effectiveness of the strengths-based approach in individualized education planning and documentation to reshape the school-educator-student alliance and accomplishspecial education goals, emphasizing the importance of incorporating input from exceptional students and their families, advocating for students' self-determination, andshifting away from the traditional pathological approach. This article calls for future research on strengths-based approaches, mental health literacy, and post-school transitions while addressing multiscalar barriersusing an intersectional lens.

A novel few-sample wind power prediction model based on generative adversarial network and quadratic mode decomposition

With the emergence of various new power systems, accurate wind power prediction plays a critical role in their safety and stability. However, due to the historical wind power data with few samples, it is difficult to ensure the accuracy of power system prediction for new wind farms. At the same time, wind power data show significant uncertainty and fluctuation. To address this issue, it is proposed in this research to build a novel few-sample wind power prediction model based on the least-square generative adversarial network (LSGAN) and quadratic mode decomposition (QMD). Firstly, a small amount of original wind power data are generated to improve the data by least-square generative adversarial network, which solves the error in prediction with limited sample data. Secondly, a quadratic mode decomposition method based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) is developed to address the instability of wind power data and extract hidden temporal characteristics. Specifically, ensemble empirical mode decomposition decomposes the data once to obtain a set of intrinsic mode functions (IMFs), and then variational mode decomposition is used to decompose the fuzzy irregular IMF1 function twice. Finally, a bidirectional long short-term memory network (BiLSTM) based on particle swarm optimization (PSO) is applied to predict wind power data. The LSGAN-QMD-PSO-BiLSTM model proposed in this research is verified on a wind farm located in Spain, which indicates that the proposed model achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) errors of 100.6577 and 66.5175 kW, along with the highest R2 of 0.9639.

Short-term Load Forecasting with Deep Learning Techniques

Short-term load forecasting is essential to the modern power system, which is the basis to guarantee the equilibrium of power supply and demand. It not only ensures the secure and stable operation of power grid, but also provides an important basis for power load dispatch, which can effectively reduce the economic loss caused by energy surplus or energy shortage in a short term. In the previous research work, traditional linear regression models, typical machine learning models and other models have been explored, but there are still problems of low accuracy or single influence factors. In this paper, a short-term forecasting model based on Temporal Convolutional Network (TCN) is proposed. Historical power load data, weather conditions, date information and their combinations are separately used to forecast the power load in different prediction models. Compared with other models, the evaluation metric of TCN model shows that its prediction accuracy is higher, and it is more suitable for short-term load forecasting.

A design of ultra-short-term power prediction algorithm driven by wind turbine operation and maintenance data for LSTM-SA neural network

Due to factors such as meteorology and geography, the generated power of wind turbines fluctuates frequently. In this way, power changes should be predicted in grid connection to take control measures in time. In this paper, an operation and maintenance data-driven LSTM-SA (long short-term memory with self-attention) prediction algorithm is designed to predict the ultra-short-term power of wind turbines. First, the wind turbine operation and maintenance data, including wind speed, blade deflection angle, yaw angle, humidity, and temperature, are subjected to feature selection by using the Pearson correlation coefficient method and the Lasso algorithm, thereby establishing the correlation between wind speed, blade deflection angle, and out power. Then, full-connect neural network is trained to establish a mapping model of wind speed, blade deflection angle, and out power. The power change rate k is calculated by the derivative of output power to wind speed. Finally, based on the historical power data and the power change rate k, the LSTM neural network power prediction model is trained to calculate the output power prediction value. In order to increase the training efficiency and reduce the delay, the self-attention mechanism is used to optimize the hidden layer of the LSTM model. The test results show that, compared with similar prediction algorithms, this algorithm has higher prediction accuracy, faster convergence speed, and better stability, which can solve the problem of accurately predicting ultra-short-term power when wind power training data is inadequate.

Wixárika Practices of Medical Syncretism: An Ontological Proposal for Health in the Anthropocene

By understanding a community’s medical system, we are able to see its body ontology and how the people within it live in relation to the world, a historically constructed ideological position. Modernisation and development have restructured Indigenous communities and devalued traditional ontologies, including medical systems. This is a global pattern, where historical power relationships defined the coloniality of being and from this, organised healthcare, governance, and education in relation to patriarchal and capitalist universals. These social structures underlie the Anthropocene geological epoch and planetary crisis. Wixárika Indigenous communities live a polytheistic sociality; their medical system treats the spiritual origins of illness, attending to social cohesion in a society of humans, the supernatural, flora and fauna. This system is subalternised by dominant universals of biomedicine, which treat the body as separate from the environment and society. I refer to this epistemological inequality as the ontological Anthropocene. Wixaritari use both allopathic and traditional medical systems, following a non-hierarchical syncretic understanding of wellbeing. Giving equal importance to both systems may be a framework with implications for wellbeing beyond human health. This Research Article proposes that by centring Indigenous sociality that is more-than-human we can reconceive our planetary relationships in the broadest sense.

An attention-based Bayesian sequence to sequence model for short-term solar power generation prediction within decomposition-ensemble strategy

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated.

Leveraging Deep Learning Algorithms for Predicting Power Outages and Detecting Faults: A Review

Power outage prediction and fault detection play crucial roles in ensuring the reliability and stability of electrical power systems. Traditional methods for predicting power outages and detecting faults rely on rule-based approaches and statistical analysis, which often fall short of accurately capturing the complex patterns and dynamics of power systems. Deep learning algorithms, with their ability to learn automatically representations from large amounts of data, have emerged as promising solutions for addressing these challenges. In this literature review, we present an overview of deep learning algorithms applied to power outage prediction and fault detection. The purpose of this literature review is to explore the uses, effectiveness and advantages and disadvantages of utilizing deep learning algorithms in this domain. Various deep-learning models were explored in the context of power outage prediction and fault detection. Convolutional Neural Networks (CNNs) are effective in analyzing spatial dependencies and patterns in power system data, such as voltage levels and load distributions. Recurrent Neural Networks (RNNs), particularly Long Short Term Memory (LSTM) networks, excel in capturing temporal dependencies and patterns in time series data such as power demand and line currents. Generative Adversarial Networks (GANs) offer a unique approach by generating synthetic power system data for training purposes. This literature review involve collecting historical power system data from various sites. Deep-learning algorithms demonstrated promising results in power outage prediction and fault detection. It achieved a high accuracy rate of 95% in predicting power outages and accurately classified various fault types with an average precision of 92%. The findings highlight the advantages of deep learning algorithms in power outage prediction and fault detection. This literature review provides a comprehensive review of deep learning algorithms in the context of power outage prediction and fault detection.

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data

AbstractThe expected growth in wind energy capacity requires efficient and accurate models for wind farm layout optimization, control, and annual energy predictions. Although analytical wake models are widely used for these applications, several model components must be better understood to improve their accuracy. To this end, we propose a Bayesian uncertainty quantification framework for physics‐guided data‐driven model enhancement. The framework incorporates turbulence‐related aleatoric uncertainty in historical wind farm data, epistemic uncertainty in the empirical parameters, and systematic uncertainty due to unmodeled physics. We apply the framework to the wake expansion parametrization in the Gaussian wake model and employ historical power data of the Westermost Rough Offshore Wind Farm. We find that the framework successfully distinguishes the three sources of uncertainty in the joint posterior distribution of the parameters. On the one hand, the framework allows for wake model calibration by selecting the maximum a posteriori estimators for the empirical parameters. On the other hand, it facilitates model validation by separating the measurement error and the model error distribution. In addition, the model adequacy and the effect of unmodeled physics are assessable via the posterior parameter uncertainty and correlations. Consequently, we believe that the Bayesian uncertainty quantification framework can be used to calibrate and validate existing and upcoming physics‐guided models.

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

AbstractClean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.

Optimal reservoir operation using the improved multi-step-ahead time-varying hedging rule under climate and land-use changes

ABSTRACT This study aims to optimize Yeywa Hydropower Reservoir (YHR) operation using the multi-step-ahead time-varying hedging (TVH) rule under climate change (CC) and land-use change (LUC) to improve summer power generation. The performance of three multi-objective algorithms – the Multi-objective Salp Swarm Algorithm (MOSSA), the Multi-objective Antlion Optimizer (MOALO) and the Non-dominated Sorting Whale Optimization Algorithm (NSOWA) – are compared. MOSSA provides the best solutions with a higher mean hypervolume in a shorter computation time, and it is utilized to optimize the TVH rules for four periods: monthly (TVH-1), quarterly (TVH-3), half-yearly (TVH-6) and yearly (TVH-12). The six-month-ahead TVH-6 rule and the five-month-ahead TVH-6 rule generate the highest summer power for the historical period (2011–2020) and the future period (2020–2059), respectively. The future decadal power generation is expected to be higher than the historical power generation. The future TVH-6 rule is more reliable and it has lower water deficits than the historical YHR operation rule.

A Call for Rethinking International Arbitration: A TWAIL Perspective on Transnationality and Epistemic Community

Despite the increasingly diversified discourses in international commercial arbitration, this device of socio-legal regulation remains a relatively under-theorized subject. In particular, far too little attention has been paid to analyzing international commercial arbitration through critical approaches such as Third World Approaches to International Law (TWAIL). TWAIL is broadly understood as a methodological reorientation in international law by highlighting the historical links between the foundations of this field of law and the history of capitalism and imperialism as well as the colonial and Eurocentric legacies in the structure and operation of the current international legal regime. With this in mind, two fronts in international commercial arbitration invite a reexamination through a TWAIL perspective and by drawing on the concept of hegemony. One front is the transnational account of arbitration, and the other one is the epistemic community of arbitration. By examining these two notions through a narrative of hegemony of Western legal traditions, we posit that any effective attempt at redefining or reforming arbitral governance structure towards sustaining diversity requires a deeper understanding of historical and current world power structures and creating a vision for the prospect of dehegemonization.

Sharia and Islamic state in Indonesia constitutional democracy: an Aceh experience

A question arises in the implementation of Sharia in Aceh: Is Sharia compatible with democracy as a political system in Indonesia? And with a comprehensive Sharia implementation, will it threaten the existence of the Indonesian state? This paper discussed Sharia's expansion, potentially leading to the establishment of the Islamic state of Aceh. This study was conducted using qualitative research. Primary data were taken from interviews with academics, journalists, non-governmental organization activists, youths, and focused group discussions with the leaders of the Islamic Sharia Office in Aceh Province. Meanwhile, the secondary data were gathered from historical documents, legislation, and Qanun in Aceh. The collected data were analyzed using the legal history theory of Pound (1923) and the Islamic democratic state theory of Hayes (2014). This research found the gradual implementation of Sharia in Aceh within the Indonesian democratic system. These constitutional ways were chosen to avoid the conflict between the Sharia and national legal and political systems. This, then, has led to the legalization of Sharia in the local and national political contestation. In the next phase, with the increasingly widespread Sharia rules and regulations on worship, criminal law, and economic law, Aceh can be called a small prototype of an Islamic state. With the significant historical, political, and social power, the roadmap of Sharia implementation in Aceh indicates a clear direction for the reflection of God's sovereignty as the foundation of the Islamic democratic state, even if it is in the local context.

Digital Identity Infrastructures: a Critical Approach of Self-Sovereign Identity

The shift from electronic identification to digital identity is indicative of a broader evolution towards datafication of identity at large. As digital identity emerges from the fringes of technical challenges towards the legal and socio-technical, pre-existing ideologies on the reform of digital identity re-emerge with a newfound enthusiasm. Self-sovereign identity is one representative example of this trend. This paper sets out to uncover the principles, technological design ideas, and underlying guiding ideologies that are attached to self-sovereign identity infrastructures, carrying the promise of user-centricity, self-sovereignty, and individual empowerment. Considering the flourishing of digital identity markets, and the subsequent institutional interest on a European level in the techno-social promises that this identity architecture carries, this paper explores how the implementation of EU-wide self-sovereign identity shifts the already existing historical power balances in the construction of identity infrastructures. In this contribution, we argue that the European-wide adoption of self-sovereign ideals in identity construction does not address the shortcomings that identity and identification have historically faced and that instead of citizen empowerment, it puts individuals (a category broader than citizens) in a rather vulnerabilized position.

A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques

South Korea has announced a plan to increase the proportion of renewable energy generation to 20% and reduce traditional energy generation by 2030. Among renewable energy resources, wind power has the advantage of relatively low power generation costs. However, it is difficult to forecast, as the output varies significantly depending on changing wind conditions such as the temperature, wind speed, and wind direction. We believe that short-term wind energy forecasts are the most important part for coping with these fluctuations and minimizing scheduling errors, thereby making the grid more reliable and reducing market service costs. Accordingly, we proposed a practical short-term wind power output forecasting method using a novel ensemble model based on a wavelet transform and autoregressive integrated moving average with explanatory variable (ARIMAX) approach. To demonstrate that the model has a good forecasting performance, we applied historical wind speed and wind power output data obtained from Jeju Island's wind farm to the model, and compared them with forecasted values. The normalized mean absolute error (NMAE) was used as the error metric. The comparison results were described for three supervisory control and data acquisition points. The average NMAE was approximately 3%. In addition, an N-1 contingency analysis was conducted to check the voltage profiles and flow limits in the context of a real power system, to ensure that the power system operated stably even with the forecasted values. The system worked successfully with the forecasted values, and can be deployed as application software for energy management systems in South Korea.

Role of Machine Learning Algorithms for Wind Power Generation Prediction in Renewable Energy Management

The electrical energy demand is growing every day. Fossil fuel-based electrical power generation pollutes the environment. So, to fulfil the electrical energy demand, clean renewable energy sources like solar and wind are emerging as viable alternatives. But the amount of power generated from solar and wind energy sources is varying in magnitude and is intermittent in nature. So, predicting solar and wind power generation is indispensable for efficient planning and controlling of a renewable energy system. This paper aims to identify a machine learning model suitable for time-series prediction of wind power and also surveys the predominantly existing machine learning followed by the deep learning approaches. The main objective of this study is to solve a real-life problem in the renewable energy sector by accurately estimating the amount of power generation production per hour by applying machine learning techniques using historical wind power energy production data. Hence, historical wind data set with input parameters like wind speed, wind direction air temperature, pressure, and output parameters of wind power generation is considered for training and testing using mean absolute error for evaluating the prediction accuracy. Layers from simple to complex deep layers are trained and tested for prediction accuracy, i.e. baseline, linear, dense, multidense, convolution neural networks, and long short-term memory. Investigating further, LSTM and Residual LSTM prove to have higher mean absolute prediction accuracy of 0.0987 and 0.0958. This eventually enlightens the wind power forecasting models and new paths for further research applications.

Respect and reflexivity: international education partnerships in primary care

ABSTRACT The UK general practice model has been described as the ‘jewel in the crown’ of the National Health Service and is widely respected and emulated around the world. In recent years, there has been a particular interest in the UK approach to primary care medical education, including at undergraduate and postgraduate levels, leading to a number of international education partnerships designed to draw on the best of UK experience and expertise in this area. Drawing on the limited academic literature in this area, and the authors’ personal experiences of working across many international partnership projects with countries around the world, this article reflects on the central importance of respect and reflexivity when engaging in such work. A respectful approach relies on a genuine and deep curiosity for the local context, and a desire to empower partners to build their own solutions that are contextually authentic. A reflexive approach, meanwhile, relies on those engaging in partnerships to understand themselves as ‘invited guests’ and to remain mindful of current and historical power differentials and inequities when framing their engagement, looking both inwardly and outwardly as they conduct themselves. As primary care education around the world develops and expands, there may be a greater role for international partnerships and it is critically important that those engaging in such partnerships bring a thoughtful and scholarly lens to this work.