Very short-term load forecaster based on a neural network technique for smart grid control

Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites

Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control

In recent years, the photovoltaic generation installed capacity has been steadily growing thanks to its inexhaustible and non-polluting characteristics. However, solar generators are strongly dependent on intermittent weather parameters, increasing power systems' uncertainty level. Forecasting models have arisen as a feasible solution to decreasing photovoltaic generators' uncertainty level, as they can produce accurate predictions. Traditionally, the vast majority of research studies have focused on the develop- ment of accurate prediction point forecasters. However, in recent years some researchers have suggested the concept of prediction interval forecasting, where not only an accurate prediction point but also the confidence level of a given prediction are computed to provide further information. This paper develops a new model for predicting photovoltaic generators' output power confidence interval 10 min ahead, based on deep learning, mathematical probability density functions and meteorological parameters. The model's accuracy has been validated with a real data series collected from Spanish meteorological sta- tions. In addition, two error metrics, prediction interval coverage percentage and Skill score, are computed at a 95% confidence level to examine the model's accuracy. The prediction interval coverage percentage values are greater than the chosen confidence level, which means, as stated in the literature, the proposed model is well-founded

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Design and Implementation of a Prognostic and Health Monitoring System for the Power Electronics Converter of a FEV Powertrain.

Prognostic and Health Monitoring Systems (PHMS) have increased their importance in the last years. Safety critical applications, such as: nuclear power plants, aerospace, railway or automotive industries, have found that PHMS increases overall system reliability and safety while reducing maintenance costs. The objective of PHMS is to determine the health state of the components under study, being able to predict their Remaining Useful Life (RUL) in order to implement advanced maintenance policies. This allows to further exploit component’s life before replacement. The increased number and variety of sensors introduced both in mechanical and electrical systems, together with the development of advanced algorithms for data treatment, allow the implementation of PHMS in a wide range of applications. The introduction of Fully Electric Vehicles (FEV) in the mainstream, have raised concerns on their reliability, mainly, on their electric and electronic components. Automotive industry is specially affected by system failure due to their high impact on customer’s image of the brand. In fact, the employment of Permanent Magnet Motors and Pulse Width Modulation inverters on new environments in which they have not been intensively tested, such as the automotive industry, suggests FEVs are candidates for PHMS implementation. In this work, a methodology was developed for PHMS implementation in FEV powertrain. A case study has been carried out on the power electronics converter to validate and test the methodology. The main contributions of this work are the discovery of failure precursor parameters and the prediction of the RUL of the components under study.Los sistemas prognósticos para la predicción y monitorización del estado de salud de sistemas complejos han atraído gran interés en los últimos tiempos. Las industrias que emplean sistemas en infraestructuras críticas para la seguridad, tales como, plantas nucleares, industria ferroviaria o aeroespacial, han descubierto su potencialidad, siendo capaces de mejorar la confiabilidad y la seguridad, así como de reducir los costes asociados al mantenimiento. El principal objetivo de los sistemas prognósticos es el de determinar el estado de salud de los componentes monitorizados, permitiendo conocer la Vida Útil Remanente (VUR), para así poder implementar políticas avanzadas de mantenimiento, alejadas del clásico mantenimiento correctivo. Esto conlleva prolongar la explotación del sistema de forma segura, reduciendo los costes debidos a las paradas no programadas y aumentando la disponibilidad. El incremento en el número y la variedad de los sensores introducidos, tanto en sistemas mecánicos como eléctricos, unido al desarrollo de algoritmos avanzados para el tratamiento de datos, ha permitido la introducción de los sistemas prognósticos en variedad de aplicaciones. La irrupción del vehículo eléctrico en el mercado, ha generado incertidumbre con respecto a su fiabilidad, mayormente, en sus componentes eléctricos y electrónicos, dada la sensibilidad de la industria automovilística en este aspecto. La industria del automóvil se ve especialmente afectada por el fallo de sistemas, debido a su impacto negativo en la percepción del cliente sobre la marca. En este sentido, el empleo de tecnologías poco testadas en estas aplicaciones, tales como motores de imanes permanentes e inversores de potencia, sugieren que el vehículo eléctrico es un candidato para la aplicación de sistemas prognósticos. En el presente trabajo, se desarrolla una metodología para la implementación de sistemas prognósticos en el tren de potencia de un vehículo eléctrico. Se ha llevado a cabo un caso de estudio en un inversor de potencia, para validar y testear la metodología. Las principales contribuciones de este trabajo son: la metodología seguida, la definición y selección de variables precursoras de fallo, así como el desarrollo de algoritmos para la predicción de la vida útil remanente de los componentes bajo estudio

Very short-term parametric ambient temperature confidence interval forecasting to compute key control parameters for photovoltaic generators

In recent years, various forecasters have been developed to decrease the uncertainty related to the intermittent nature of photovoltaic generation. While the vast majority of these forecasters are usually just focused on deterministic or probabilistic prediction points, few studies have been carried out in relation to prediction intervals. In increasing the reliability of photovoltaic generators, being able to set a confidence level is as important as the forecaster’s accuracy. For instance, changes in ambient temperature or solar irradiation produce variations in photovoltaic generators’ output power as well as in control parameters such as cell temperature and open voltage circuit. Therefore, the aim of this paper is to develop a new mathematical model to quantify the confidence interval of ambient temperature in the next 10 min. Several error metrics, such as the prediction interval coverage percentage, the Winkler score and the Skill score, are calculated for 95%, 90% and 85% confidence levels to analyse the reliability of the developed model. In all cases, the prediction interval coverage percentage is higher than the selected confidence interval, which means that the estimation model is valid for practical photovoltaic applications

Very short-term temperature forecaster using MLP and N-nearest stations for calculating key control parameters in solar photovoltaic generation

Although photovoltaic generation has been proposed as a solution for the world’s energy challenges, it depends to a large extent on solar irradiation and air temperature. Therefore, small variations in these meteorological parameters produce sudden changes in power generation, which makes it difficult to integrate photovoltaic generators into the electrical grid. The aim of this study is to develop a very short-term temperature forecaster that makes photovoltaic generation more reliable in order to provide not only power but also ancillary services. To predict ambient temperature in a specific area (Vitoria-Gasteiz, Basque Country) in the next 10 min, this forecaster combines a multilayer perceptron and the optimal nearest number of meteorological. In addition, the distance and relative location between each station and the target station were taken into account. The accu- mulated deviation between actual and forecasted temperature was lower than 1% in 96.60% of the examined days from the validation database. Moreover, the root mean square error was 0.2557 ◦C, which represents an improvement of 13.20% as compared with the benchmark result. The results indicated that the forecaster can be considered for implementation in photovoltaic generators to compute key control parameters and improve their integration into the electrical grid

Very short-term temperature forecaster using MLP and N-nearest stations for calculating key control parameters in solar photovoltaic generation

Although photovoltaic generation has been proposed as a solution for the world’s energy challenges, it depends to a large extent on solar irradiation and air temperature. Therefore, small variations in these meteorological parameters produce sudden changes in power generation, which makes it difficult to integrate photovoltaic generators into the electrical grid. The aim of this study is to develop a very short-term temperature forecaster that makes photovoltaic generation more reliable in order to provide not only power but also ancillary services. To predict ambient temperature in a specific area (Vitoria-Gasteiz, Basque Country) in the next 10 min, this forecaster combines a multilayer perceptron and the optimal nearest number of meteorological. In addition, the distance and relative location between each station and the target station were taken into account. The accu- mulated deviation between actual and forecasted temperature was lower than 1% in 96.60% of the examined days from the validation database. Moreover, the root mean square error was 0.2557 ◦C, which represents an improvement of 13.20% as compared with the benchmark result. The results indicated that the forecaster can be considered for implementation in photovoltaic generators to compute key control parameters and improve their integration into the electrical grid

Very short-term load forecaster based on a neural network technique for smart grid control

Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites

Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power

Photovoltaic generation has arisen as a solution for the present energy challenge. However, power obtained through solar technologies has a strong correlation with certain meteorological variables such as solar irradiation, wind speed or ambient temperature. As a consequence, small changes in these variables can produce unexpected deviations in energy production. Although many research articles have been published in the last few years proposing different models for predicting these parameters, the vast majority of them do not consider spatiotemporal parameters. Hence, this paper presents a new solar irradiation forecaster which combines the advantages of machine learning and the optimisation of both spatial and temporal parameters in order to predict solar irradiation 10 min ahead. A validation step demonstrated that the deviation between the actual and forecasted solar irradiation was lower than 4% in 82.95% of the examined days. With regard to the error metrics, the root mean square error was 50.80 W/m2, an improvement of 11.27% compared with the persistence model, which was used as a benchmark. The results indicate that the developed forecaster can be integrated into photovoltaic generators’ to predict their output power, thus promoting their inclusion in the main power network

Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power

Photovoltaic generation has arisen as a solution for the present energy challenge. However, power obtained through solar technologies has a strong correlation with certain meteorological variables such as solar irradiation, wind speed or ambient temperature. As a consequence, small changes in these variables can produce unexpected deviations in energy production. Although many research articles have been published in the last few years proposing different models for predicting these parameters, the vast majority of them do not consider spatiotemporal parameters. Hence, this paper presents a new solar irradiation forecaster which combines the advantages of machine learning and the optimisation of both spatial and temporal parameters in order to predict solar irradiation 10 min ahead. A validation step demonstrated that the deviation between the actual and forecasted solar irradiation was lower than 4% in 82.95% of the examined days. With regard to the error metrics, the root mean square error was 50.80 W/m2, an improvement of 11.27% compared with the persistence model, which was used as a benchmark. The results indicate that the developed forecaster can be integrated into photovoltaic generators’ to predict their output power, thus promoting their inclusion in the main power network