Search Results (14)

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3). Full article

Marine transportation accounts for approximately 90% of the total trade managed in international logistics and plays a vital role in many companies’ supply chains. However, en-route factors like weather conditions or piracy incidents often delay scheduled arrivals at destination ports, leading to downstream inefficiencies. Due to the maritime industry’s digital transformation, smart ports and vessels generate vast amounts of data, creating an opportunity to use the latest technologies, like machine and deep learning (ML/DL), to support terminals in their operations. This study proposes a data-driven solution for accurately predicting vessel arrival times using ML/DL techniques, including Deep Neural Networks, K-Nearest Neighbors, Decision Trees, Random Forest, and Extreme Gradient Boosting. This study collects real-world AIS data in the Eastern Mediterranean Sea from a network of public and private AIS base stations. The most relevant features are selected for training and evaluating the six ML/DL models. A comprehensive comparison is also performed against the estimated arrival time provided by shipping agents, a simple calculation-based approach, and four other ML/DL models proposed recently in the literature. The evaluation has revealed that Random Forest achieves the highest performance with an MAE of 99.9 min, closely followed by XGBoost, having an MAE of 105.0 min. Full article

The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from climate-related monitoring products obtained from an integration of remote sensing and in situ data from weather stations. It contains useful indicators, such as rainfall, vegetation, and fire conditions, that provide an overview of conditions across the country. The present study demonstrates how these natural resource indices are integrated and consolidated for utilization by farmers, policy-makers, private organizations, and the general public to make day-to-day decisions on the management and mitigation of natural disasters. However, there is a need to expand these baseline observation initiatives, including the following: (1) forecasting future conditions to strengthen coping mechanisms of government, farmers, and communities at large; and (2) incorporating information on other natural disasters such as floods and extreme heat. In the context of South Africa, this information is important to improve disaster preparedness and management for agricultural productivity. In a global context, the Umlindi newsletter can be insightful for developing and disseminating natural resources information on adaptation to and mitigation of climate change and variability impacts to other regions facing similar risks. Furthermore, while international organizations also provide natural resource information, the Umlindi newsletter may be distinguished by its regional focus and linkages to individual communities. It bridges the gap between global environmental data and local decision-making by illustrating how global scientific knowledge may be applied locally. Full article

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, UT, USA. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum $R^2$ scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable. Full article

Many German cities, municipalities and transport associations are expanding their bike-sharing systems (BSS) to offer citizens a cost-effective and climate-friendly means of transport and an alternative to private motorized transport (PMT). However, operators face the challenge of generating high-quality predictive analyses and time series forecasts. In particular, the prediction of demand is a key component to foster data-driven decisions. To address this problem, an Unobserved Component Model (UCM) has been developed to predict the monthly rentals of a BSS, whereby the station-based BSS VRNnextbike, including over 2000 bikes, 297 stations and 21 municipalities, is employed as an example. The model decomposes the time series into trend, seasonal, cyclical, auto-regressive and irregular components for statistical modeling. Additionally, the model includes exogenous factors such as weather, user behavior (e.g., traveled distance), school holidays and COVID-19 relevant covariates as independent effects to calculate scenario based forecasts. It can be shown that the UCM calculates reasonably accurate forecasts and outperforms classical time series models such as ARIMA(X) or SARIMA(X). Improvements were observed in model quality in terms of AIC/BIC (2.5% to 22%) and a reduction in error metrics from 15% to 45% depending on the considered model. Full article

The number of people owning a private weather station (PWS) and sharing their meteorological measurements online is growing worldwide. This leads to an unprecedented high density of weather observations, which could help monitor and understand small-scale weather phenomena. However, good data quality cannot be assured and thorough quality control is crucial before the data can be utilized. Nevertheless, this type of data can potentially be used to supplement conventional weather station networks operated by National Meteorological & Hydrological Services (NMHS), since the demand for high-resolution meteorological applications is growing. This is why EUMETNET, a community of European NMHS, decided to enhance knowledge exchange about PWS between NMHSs. Within these efforts, we have collected information about the current interest in PWS across NMHSs and their experiences so far. In addition, this paper provides an overview about the data quality challenges of PWS data, the developed quality control (QC) approaches and openly available QC tools. Some NMHS experimented with PWS data, others have already incorporated PWS measurements into their operational workflows. The growing number of studies with promising results and the ongoing development of quality control procedures and software packages increases the interest in PWS data and their usage for specific applications. Full article

Bike-sharing systems implemented in cities with good bike lane networks could potentiate a modal shift from short car trips, boosting sustainable mobility. Both passenger and last-mile goods transportation can benefit from such systems and, in fact, bike sharing (dockless or with docking stations) is increasing worldwide, especially in Europe. This research focused on a European city, Lisbon, and the e-bike sharing system GIRA, in its early deployment, in 2018, where it had about 409 bikes of which 30% were non-electric conventional bikes and 70% were e-bikes. The research aims at answering the main research questions: (1) What is the number of trips per day and travel time in conventional bikes and e-bikes?; (2) Do the daily usage peaks follow the trends of other modes of transport in terms of rush hours?; (3) Are there seasonality patterns in its use (weekdays and weekends, workdays and holiday periods)?; (4) How do climate conditions affect its use?; and finally, (5) What would be the impact on final energy consumption and GHG emissions? The dataset for 2018 regarding GIRA trips (distance, time, conventional or e-bike, docking station origin and destination) and weather (temperature, wind speed, relative humidity, precipitation) was available from Lisbon City Hall by means of the program “Lisboa aberta”. Data regarding the profile of the users (which trips GIRA replaces?) and data regarding electricity consumption were not available. The latter was estimated by means of literature e-bike data and electric motor specifications combined with powertrain efficiency. Greenhouse gas (GHG) emissions were estimated by using the latest Intergovernmental Panel on Climate Change (IPCC) CO₂ equivalents and a spreadsheet simulator for the Portuguese electricity GHG intensity, which was adaptable to other countries/locations. In a private car fleet dominated by fossil fuels and internal combustion engines, the e-bike sharing system is potentially avoiding 36 Ton GHG/year and reducing the energy consumption by 451 GJ/year. If the modal shift occurs from walking or urban bus to an e-bike sharing system, the impact will be detrimental for the environment. Full article

Rising temperatures worldwide pose an existential threat to people, properties, and the environment. Urban areas are particularly vulnerable to temperature increases due to the heat island effect, which amplifies local heating. Throughout the world, several megacities experience summer temperatures that stress human survival. Generating very high-resolution temperature forecasts is a fundamental problem to mitigate the effects of urban warming. This paper uses the Analog Ensemble technique to downscale existing temperature forecast from a low resolution to a much higher resolution using private weather stations. A new downscaling approach, based on the reuse of the Analog Ensemble (AnEn) indices, resulted by the combination of days and Forecast Lead Time (FLT)s, is proposed. Specifically, temperature forecasts from the NAM-NMM Numerical Weather Prediction model at 12 km are downscaled using 83 Private Weather Stations data over Manhattan, New York City, New York. Forecasts for 84 h are generated, hourly for the first 36 h, and every three hours thereafter. The results are dense forecasts that capture the spatial variability of ambient conditions. The uncertainty associated with using non-vetted data is addressed. Full article

Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties >0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements. Full article

We report the results on factors for self-protective behavior against weather extremes such as extreme heat events, drought, and heavy precipitation. Our research draws on the Model of Private Proactive Adaptation to Climate Change (MPPACC). We developed a survey instrument incorporating the main aspects of the MPPACC and other factors from related research work that are assumed to explain why some people show self-protective behavior while others do not. The interview survey was conducted with a non-random sample of 210 respondents from three Philippine cities, namely Baguio, Dagupan, and Tuguegarao. The results reveal the importance of adaptation appraisal, including the perceived feasibility of self-protective measures, the perceived adaptation knowledge, and, with limitations, the perception of actions taken by neighbors or friends. We also show that perceptions of past weather trends are closely linked to risk perception but are only partly corroborated by weather station data. Implications for fostering self-protective behavior are making use of time windows right after an extreme weather event and focusing on enhancing adaptation appraisal. Full article

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency. Full article

The number of natural catastrophes that affect people worldwide is increasing; among these, the hydro-meteorological events represent the worst scenario due to the thousands of deaths and huge damages to private and state ownership they can cause. To prevent this, besides various structural measures, many non-structural solutions, such as the implementation of flood warning systems, have been proposed in recent years. In this study, we suggest a low computational cost method to produce a probabilistic flood prediction system using a single forecast precipitation scenario perturbed via a spatial shift. In fact, it is well-known that accurate forecasts of heavy precipitation, especially associated with deep moist convection, are challenging due to uncertainties arising from the numerical weather prediction (NWP), and high sensitivity to misrepresentation of the initial atmospheric state. Inaccuracies in precipitation forecasts are partially due to spatial misplacing. To produce hydro-meteorological simulations and forecasts, we use a flood forecasting system which comprises the physically-based rainfall-runoff hydrological model FEST-WB developed by the Politecnico di Milano, and the MOLOCH meteorological model provided by the Institute of Atmospheric Sciences and Climate (CNR-ISAC). The areas of study are the hydrological basins of the rivers Seveso, Olona, and Lambro located in the northern part of Milan city (northern Italy) where this system works every day in real-time. In this paper, we show the performance of reforecasts carried out between the years 2012 and 2015: in particular, we explore the ‘Shift-Target’ (ST) approach in order to obtain 40 ensemble members, which we assume equally likely, derived from the available deterministic precipitation forecast. Performances are shown through statistical indexes based on exceeding the threshold for different gauge stations over the three hydrological basins. Results highlight how the Shift-Target approach complements the deterministic MOLOCH-based flood forecast for warning purposes. Full article

The goal of the research is to assess the minimum requirement of fast charging infrastructure to allow country-wide interurban electric vehicle (EV) mobility. Charging times comparable to fueling times in conventional internal combustion vehicles are nowadays feasible, given the current availability of fast charging technologies. The main contribution of this paper is the analysis of the planning method and the investment requirements for the necessary infrastructure, including the definition of the Maximum Distance between Fast Charge (MDFC) and the Basic Highway Charging Infrastructure (BHCI) concepts. According to the calculations, distance between stations will be region-dependent, influenced primarily by weather conditions. The study considers that the initial investment should be sufficient to promote the EV adoption, proposing an initial state-financed public infrastructure and, once the adoption rate for EVs increases, additional infrastructure will be likely developed through private investment. The Spanish network of state highways is used as a case study to demonstrate the methodology and calculate the investment required. Further, the results are discussed and quantitatively compared to other incentives and policies supporting EV technology adoption in the light-vehicle sector. Full article

In Asia and Africa the poor tend to live in marginal environments where droughts and floods are frequent. Global warming is expected to increase the frequency of these weather-induced perturbations of crop production. Drought tolerance (DT) has been one of the most difficult traits to improve in genetic crop improvement programs worldwide. Biotechnology provides breeders with a number of new tools that may help to develop more drought tolerant varieties such as marker assisted selection (MAS), molecular breeding (MB), and transgenic plants. This paper assesses some preliminary evidence on the potential impact of biotechnology using data from surveys of the initial DT cultivars developed through one of the main programs in Asia that has been funding DT rice breeding since 1998—The Rockefeller Foundation’s Resilient Crops for Water-Limited Environments program in China, India, and Thailand. Yield increases of DT rice varieties are 5 to 10 percent better than conventional varieties or currently grown commercial varieties than it has been in years. So far we only have experiment station evidence that DT varieties yielded better than conventional or improved varieties during moderate drought years (the one drought year during our study period in South India gave inconclusive results) and in severe drought both the DT and the conventional varieties were either not planted or, if planted, did not yield. We find that the governments could help overcome some of the constraints to the spread of DT cultivars by increasing government funding of DT research programs that take advantage of new biotech techniques and new knowledge from genomics. Secondly, public scientists can make breeding lines with DT traits and molecular markers more easily available to the private seed firms so that they can incorporate DT traits into their commercial hybrids particularly for poor areas. Third, governments can subsidize private sector production of DT seed or provide more government money for state extension services to produce DT varieties. Full article