Through the application of cloud top temperature data and the extraction of supercooled cloud information in cloud-type data from the next-generation Himawari-8 geostationary satellite with high spatial–temporal resolution, a quantitative evalu...
The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every...
In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the mai...
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-te...
Air pollution, particularly fine particulate matter (PM2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Communi...
Heavy rainfall events are the main cause of flooding, especially in regions like Kerala, India. Kerala is vulnerable to extreme weather due to its geographical location in the Western Ghats. Accurate forecasting of rainfall events is essential for mi...
Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. Th...
High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the cr...
Accurately predicting building heating and cooling loads is crucial for optimizing HVAC systems and enhancing energy efficiency. However, data-driven models often face overfitting issues due to scarce training data, a common challenge for new constru...
The Weather Research and Forecasting (WRF) model is a mesoscale numerical weather prediction system, which is widely used in major high-performance server platforms. This study focuses on the performance evaluation and optimization of WRF on Huawei&r...
Accurately forecasting sinusoidal time series is essential in various scientific and engineering applications. However, traditional models such as the seasonal autoregressive integrated moving average (SARIMA) rely on assumptions of linearity and sta...
On 27 February 2023, the municipality of Itajubá in southeastern Brazil experienced a short-duration yet high-intensity rainfall event, causing significant socio-economic impacts. Hence, this study evaluates the performance of the Weather Rese...
In today’s data-driven world, where information is one of the most valuable resources, forecasting the behavior of time series, collected by modern sensor networks and IoT systems, is crucial across various fields, including finance, climatolog...
The current work presents the operational implementation and evaluation of a rapid response fire spread forecasting system, named IRIS, that was developed to provide support to the tactical wildfire suppression activities of the Hellenic Fire Corps....
The super-efficiency data envelopment analysis model is innovative in evaluating the performance of crude oil prices’ volatility forecasting models. This multidimensional ranking, which takes account of multiple criteria, gives rise to a unifie...
Accurate and interpretable short-term load forecasting tasks are essential to the optimal operation of liberalized electricity markets since they contribute to the efficient development of energy trading and demand response strategies as well as the...
28 August 2023
Accurate short-term water consumption forecasting is a crucial function of modern water supply systems, which, in turn, play a crucial role in the sustainable management of water resources, particularly in regions with limited access to water supplie...
The accurate short-term forecasting of global horizontal irradiance (GHI) is essential to optimizing the operation and integration of solar energy systems into the power grid. This study evaluates the performance of the Weather Research and Forecasti...
2 January 2020
The paper presents the results of the development of the cardio-forecasting technology, which introduces a new method to monitor the state of human-operator, which is characteristic for the given production conditions and for individual operators, to...
Machine learning (ML) models utilizing high-frequency Internet of Things (IoT) monitoring data offer significant potential for stream-level nowcasting. The assessment of model performance is often a challenging task because conventional approaches ar...
Accurate forecast of PM2.5 pollution is highly needed for the timely prevention of haze pollution in many cities suffered from frequent haze pollution. In this work, an online recurrent extreme learning machine (OR-ELM) technique with online data upd...
Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cool...
Time series forecasting performance is strongly influenced by the structural properties of the underlying data, yet learning-based models are often applied without sufficient validation of this dependency. This study evaluates a uniformly configured...
Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly sav...
The fast growth of photovoltaic (PV) power generation requires dependable forecasting methods to support efficient integration of solar energy into power systems. This study conducts an up-to-date, systematized analysis of different models and method...
The National Water Model (NWM)’s streamflow forecasts are widely used by stakeholders to make critical water management decisions. This study evaluates the performance of the NWM v2.1 in simulating streamflow across the Alabama Black Belt Regio...
A maximum instantaneous wind speed forecast methodology based on the autoregressive with exogenous inputs (ARX) model is proposed, in which numerical weather prediction and on-site measurement are used as inputs and the model parameters are estimated...
Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This pa...
The Information and Communication Industry (ICT) plays a very important role in the growth of any nation. Therefore, the ITC sector attracts the great attention of the researcher. Despite many breakthroughs and favorable conditions to develop, the In...
The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the e...
With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-...
Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparame...
This paper analyzes the forecast performance of historical S&P500 and Dow Jones Industrial Average (DJIA) excess returns while using nonparametric functional data analysis (NP-FDA). The empirical results show that the NP-FDA forecasting strategy...
This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to correspon...
Healthcare supply chains are complex systems facing challenges in delivering high-quality care while maintaining cost-effectiveness. Research on inventory management and control, the heart of the healthcare supply system, has demonstrated that these...
With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is propo...
One of the key challenges for (fresh produce) retailers is achieving optimal demand forecasting, as it plays a crucial role in operational decision-making and dampens the Bullwhip Effect. Improved forecasts holds the potential to achieve a balance be...
This study evaluates the performance of the Numerical Tools for Hurricane Forecast (NTHF) system during the 2020 North Atlantic (NATL) tropical cyclones (TCs) season. The system is configured to provide 5-day forecasts with basic input from the Natio...
This paper proposes a hierarchical CNN-sLSTM-Attention model for long-sequence time series forecasting. It enhances efficiency by replacing traditional LSTMs with a stable LSTM (sLSTM) variant, which incorporates exponential gating and memory mixing....
The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast S...
This study evaluates a Weather Research and Forecasting (WRF) system with a 500 m horizontal grid spacing developed to provide mountain-focused rainfall guidance for forest disaster preparedness in South Korea. The system uses operational 1.5 km Loca...
Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models...
In response to declining sea ice cover, human activity in the Arctic is increasing, with access to the Arctic Ocean becoming more important for socio-economic reasons. Accurate knowledge of sea ice conditions is therefore becoming increasingly import...
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forec...
The selection of an accurate performance metric is highly important to evaluate the quality of a forecasting method. This evaluation may help to select between different forecasting tools of forecasting outputs, and then support many decisions within...
Pangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predic...
In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support...
Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentr...
Accurate forecasting of domestic air passenger demand can support transportation planning, infrastructure development, and operational decision-making. However, passenger demand may be influenced by economic, tourism-related, technological, and mobil...
This study introduces a Transformer-based forecasting tool termed EXPERT (EXchange rate Prediction using Encoder Representation from Transformers) and applies it to exchange rate forecasting. We developed and trained a Transformer-based forecasting m...
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