Short-term wind speed forecasting in Uruguay using computational intelligence.
ABSTRACT: Short-term wind speed forecasting for Colonia Eulacio, Soriano Department, Uruguay, is performed by applying an artificial neural network (ANN) technique to the hourly time series representative of the site. To train the ANN and validate the technique, data for one year are collected by one tower, with anemometers installed at heights of 101.8, 81.8, 25.7, and 10.0 m. Different ANN configurations are applied for each site and height; then, a quantitative analysis is conducted, and the statistical results are evaluated to select the configuration that best predicts the real data. This method has lower computational costs than other techniques, such as numerical modelling. For integrating wind power into existing grid systems, accurate short-term wind speed forecasting is fundamental. Therefore, the proposed short-term wind speed forecasting method is an important scientific contribution for reliable large-scale wind power forecasting and integration in Uruguay. The results of the short-term wind speed forecasting showed good accuracy at all the anemometer heights tested, suggesting that the method is a powerful tool that can help the Administración Nacional de Usinas y Transmissiones Eléctricas manage the national energy supply.
Project description:Wind energy harvesting technology is one of the most popular power sources for wireless sensor networks. However, given its irregular nature, wind energy availability experiences significant variations and, therefore, wind-powered devices need reliable forecasting models to effectively adjust their energy consumption to the dynamics of energy harvesting. On the other hand, resource-constrained devices with limited hardware capacities (such as sensor nodes) must resort to forecasting schemes of low complexity for their predictions in order to avoid squandering their scarce power and computing capabilities. In this paper, we present a new efficient ARIMA-based forecasting model for predicting wind speed at short-term horizons. The performance results obtained using real data sets show that the proposed ARIMA model can be an excellent choice for wind-powered sensor nodes due to its potential for achieving accurate enough predictions with very low computational burden and memory overhead. In addition, it is very simple to setup, since it can dynamically adapt to varying wind conditions and locations without requiring any particular reconfiguration or previous data training phase for each different scenario.
Project description:The data presented in this article are the wind measurements acquired from a tower in Southeast China during typhoon Nesat (1709#) and typhoon Haitang (1710#). Three 3D ultrasonic anemometers Wind Master Pro were utilized to obtain 3D wind data. The anemometer works well with wind speed range of 0-65?m/s and wind angle range of 0-360°. Three direction wind speeds and wind angles were recorded per every 0.1?s. The present research analyzed wind characteristics based on recorded data. In this article, the detailed test set-up and data pre-processing methodology for the wind characteristics analysis are provided.
Project description:Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.
Project description:The rapid development of the Internet of Things (IoT) has tremendously increased the demands for wind speed sensors in various applications, such as weather forecasting and environmental monitoring. To date, many wind speed sensors are developed based on triboelectric nanogenerators (TENGs). However, the low output current leads to a poor sensing precision, which greatly limits their practical applications. Here, a wind speed sensor is proposed by integrating a wind-driven triboelectrification-induced electroluminescence (TIEL) component with a perovskite-based photodetector (PD) for enlarged electric current output. Compared with mechanoluminescence (ML), TIEL displays significantly higher signal intensity even under gentle breezes. In addition, the emission peak of TIEL matches with the absorption band of perovskite materials. Thus, TIEL materials are promising candidates for generating distinct electrical signals in real-time to facilitate the detection of wind speed. With a comparable detection limit (5 m s-1) to a conventional TENG-based wind speed sensor, the sensitivity of this hybrid sensor is three magnitudes higher at low bias voltages and, the response time is extremely short (<0.3 s). Moreover, it shows a favorable correlation with a commercial sensor. This research provides important progress toward environmentally-friendly light sources and TIEL-related sensor systems.
Project description:Wind turbines represent a source of hazard for bats, especially through collision with rotor blades. With increasing technical development, tall turbines (rotor-swept zone 50-150 m above ground level) are becoming widespread, yet we lack quantitative information about species active at these heights, which impedes proposing targeted mitigation recommendations for bat-friendly turbine operation. We investigated vertical activity profiles of a bat assemblage, and their relationships to wind speed, within a major valley of the European Alps where tall wind turbines are being deployed. To monitor bat activity we installed automatic recorders at sequentially increasing heights from ground level up to 65 m, with the goal to determine species-specific vertical activity profiles and to link them to wind speed. Bat call sequences were analysed with an automatic algorithm, paying particular attention to mouse-eared bats (Myotis myotis and Myotis blythii) and the European free-tailed bat (Tadarida teniotis), three locally rare species. The most often recorded bats were the Common pipistrelle (Pipistrellus pipistrellus) and Savi's pipistrelle (Hypsugo savii). Mouse-eared bats were rarely recorded, and mostly just above ground, appearing out of risk of collision. T. teniotis had a more evenly distributed vertical activity profile, often being active at rotor level, but its activity at that height ceased above 5 ms-1 wind speed. Overall bat activity in the rotor-swept zone declined with increasing wind speed, dropping below 5% above 5.4 ms-1. Collision risk could be drastically reduced if nocturnal operation of tall wind turbines would be restricted to wind speeds above 5 ms-1. Such measure should be implemented year-round because T. teniotis remains active in winter. This operational restriction is likely to cause only small energy production losses at these tall wind turbines, although further analyses are needed to assess these losses precisely.
Project description:Solar dimming and wind stilling (slowdown) are two outstanding climate changes occurred in China over the last four decades. The wind stilling may have suppressed the dispersion of aerosols and amplified the impact of aerosol emission on solar dimming. However, there is a lack of long-term aerosol monitoring and associated study in China to confirm this hypothesis. Here, long-term meteorological data at weather stations combined with short-term aerosol data were used to assess this hypothesis. It was found that surface solar radiation (SSR) decreased considerably with wind stilling in heavily polluted regions at a daily scale, indicating that wind stilling can considerably amplify the aerosol extinction effect on SSR. A threshold value of 3.5 m/s for wind speed is required to effectively reduce aerosols concentration. From this SSR dependence on wind speed, we further derived proxies to quantify aerosol emission and wind stilling amplification effects on SSR variations at a decadal scale. The results show that aerosol emission accounted for approximately 20% of the typical solar dimming in China, which was amplified by approximately 20% by wind stilling.
Project description:Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB(3)GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB(3)GEO forecasts use solar wind density and interplanetary magnetic field B z observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB(3)GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB(3)GEO forecast.
Project description:<h4>Background</h4>Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods-ARIMA regression and Holt-Winters exponential smoothing models.<h4>Methods</h4>The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the official website of World Bank. The optimal models for each forecasting methods were used for forecasting mortality rates to 2030 (ending of Sustainable Development Goal era). The predictive performances of the three methods were evaluated, based on root mean squared errors (RMSE), root mean absolute error (RMAE) and modified Nash-Sutcliffe efficiency (NSE) coefficient. Statistically significant differences in loss function between forecasts of GMDH-type ANN model compared to each of the ARIMA and Holt-Winters models were assessed with Diebold-Mariano (DM) test and Deming regression.<h4>Results</h4>The modified NSE coefficient was slightly lower for Holt-Winters methods (96.7%), compared to GMDH-type ANN (99.8%) and ARIMA (99.6%). The RMSE of GMDH-type ANN (0.09) was lower than ARIMA (0.23) and Holt-Winters (2.87). Similarly, RMAE was lowest for GMDH-type ANN (0.25), compared with ARIMA (0.41) and Holt-Winters (1.20). From the DM test, the mean absolute error (MAE) was significantly lower for GMDH-type ANN, compared with ARIMA (difference?=?0.11, p-value?=?0.0003), and Holt-Winters model (difference?=?0.62, p-value<?0.001). Based on the intercepts from Deming regression, the predictions from GMDH-type ANN were more accurate (?<sub>0</sub> =?0.004?±?standard error: 0.06; 95% confidence interval: -?0.113 to 0.122).<h4>Conclusions</h4>GMDH-type neural network performed better in predicting and forecasting of under-five mortality rates for Nigeria, compared to the ARIMA and Holt-Winters models. Therefore, GMDH-type ANN might be more suitable for data with non-linear or unknown distribution, such as childhood mortality. GMDH-type ANN increases forecasting accuracy of childhood mortalities in order to inform policy actions in Nigeria.
Project description:Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model.A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970-2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE).Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (? = 0.021, 95% confidence interval(CI): 0.004-0.038) and rainfall with 12-month lag (? = 0.023, 95% CI: 0.009-0.037) displayed the highest prediction accuracy.The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity.
Project description:An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.