We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is exten...
This study developed a hydrological drought forecasting framework linked to the meteorological model and land surface model (LSM) considering hydrologic facilities and evaluated the feasibility of the Modified Surface Water Supply Index (MSWSI) for d...
6 September 2018
Air pollution forecasting plays a vital role in environment pollution warning and control. Air pollution forecasting studies can also recommend pollutant emission control strategies to mitigate the number of poor air quality days. Although various li...
The contribution of this article is mainly to develop a new stochastic sequence forecasting model, which is also called the difference iterative forecasting model based on the Generalized Cauchy (GC) process. The GC process is a Long-Range Dependent...
The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which...
In the field of photovoltaic (PV) power prediction, long-term forecasting, which is more challenging than short-term forecasting, can provide more comprehensive and forward-looking guidance. Currently, significant achievements have been made in the f...
Recent studies have shown the potential of transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Lo...
Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the fl...
Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself...
This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperfo...
For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no lo...
The analysis of residual life expectancy evolution at retirement age holds great importance for life insurers and pension schemes. Over the last 30 years, numerous models for forecasting mortality have been introduced, and those that allow us to pred...
The traditional combination forecasting model has good forecasting effect, but it needs precise historical data. In fact, many random events are uncertain, and much of the data are imprecise; sometimes, historical data are lacking. We need to study c...
Reliable forecasting of tropical cyclone (TC) intensity—particularly rapid intensification (RI) events—remains a major challenge in meteorology, largely due to the inherent difficulty of accurately quantifying predictive uncertainty. Trad...
In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, mark...
Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combinatio...
Due to inherent randomness and fluctuation of wind speeds, it is very challenging to develop an effective and practical model to achieve accurate wind speed forecasting, especially over large forecasting horizons. This paper presents a new decomposit...
The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecastin...
Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble le...
Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision makers, as well as improve the carbon market system. Nevertheless, existing methods for combin...
Because of differences in the underlying surface, short flood confluence times, extreme precipitation, and other dynamic parameters, it is difficult to forecast an inflow flood to a basin reservoir, and traditional hydrological models do not achieve...
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 forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new m...
Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model fo...
30 October 2022
In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the eff...
Amongst energy-related CO
The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a s...
With an increasing emphasis on energy conservation, emission reduction, and power consumption management, port enterprises are focusing on enhancing their electricity load forecasting capabilities. Accurate electricity load forecasting is crucial for...
Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA)...
The increasing frequency and duration of severe fire events in Australia further necessitate accurate and timely forecasting to mitigate their consequences. This study evaluated the performance of two distinct approaches to forecasting extreme fire d...
Meteorological forcing is the primary driving force and primary source of errors for storm surge forecasting. The objective of this study was to learn how forecasted meteorological forcing influences storm surge generation and propagation during a hu...
Prediction of electricity consumption plays critical roles in the economy. Accurate electricity consumption forecasting is essential for policy makers to formulate electricity supply policies. However, limited data and variables generally cannot prov...
With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition of the importance of financial time series forecasting in financial market operati...
Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the exi...
Air pollution, outstanding particulate matter (PM2.5), poses severe risks to human health and the environment in densely populated urban areas. Accurate short-term forecasting of PM2.5 concentrations is therefore crucial for timely public health advi...
This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach d...
Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special c...
This study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias cor...
COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help he...
Solar power’s variability makes managing power system planning and operation difficult. Facilitating a high level of integration of solar power resources into a grid requires maintaining the fundamental power system so that it is stable when in...
Accurate short-term wind speed forecasting is essential for mitigating wind power variability and supporting stable grid operation. This study proposes a model selection forecasting system (MSFS) that dynamically integrates six deep learning models t...
This research evaluates the quality of high-impact rainfall forecasts across Vietnam and its sub-climate regions. The 3-day rainfall forecast products evaluated include the European Centre for Medium-Range Weather Forecasts (ECMWF) High-Resolution In...
Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems, particularly with the increasing integration of renewable energy and the transition toward smart grids. However, most existing...
Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing...
Accurate interval forecasting of wind power is crucial for ensuring the safe, stable, and cost-effective operation of power grids. In this paper, we propose a hybrid deep learning model for day-ahead wind power interval forecasting. The model begins...
The predictability of wind energy is crucial due to the uncertain and intermittent features of wind energy. This study proposes wind speed forecasting models, which employ time series clustering approaches and deep learning methods. The deep learning...
The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integratio...
Since entering the 21st century, “economic globalization” has become a hot topic. Under the impact of “economic globalization”, the competition of the Chinese domestic market continues to intensify, and Chinese enterprises are...
Accurate cooling load forecasting in high-efficiency chiller plants with ice storage systems is essential for intelligent control, energy conservation, and maintaining indoor comfort. However, conventional forecasting methods often struggle to model...
Forecasting macroeconomic variables is essential to macroeconomics, financial economics, and monetary policy analysis. Due to the high dimensionality of the macroeconomic dataset, it is challenging to forecast efficiently and accurately. Thus, this s...
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