Reliable forecast precipitation can support disaster prevention and mitigation and sustainable socio-economic development. Improving forecast precipitation accuracy remains a challenge. Therefore, a novel method for multi-model forecast precipitation...
In the burgeoning field of sustainable energy, this research introduces a novel approach to accurate medium- and long-term load forecasting in large-scale power systems, a critical component for optimizing energy distribution and reducing environment...
The uncertainties in source-side renewable power supply and user-side multi-energy demand pose significant challenges to coordinated scheduling in an electricity–heat–hydrogen integrated energy system (EHH-IES). A hierarchical scheduling...
Stock market performance is one key indicator of the economic condition of a country, and stock price forecasting is important for investments and financial risk management. However, the inherent nonlinearity and complexity in stock price movements i...
Air pollution forecasting remains a critical challenge for urban public health management, with traditional approaches struggling to balance accuracy and interpretability. This study introduces a novel PM10 forecasting framework combining physics-inf...
Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for co...
Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive int...
Forecasting national energy demand is challenging under climate variability and macroeconomic uncertainty. We assess whether hybrid Prophet–(Q)LSTM models that integrate climate and economic predictors improve long-horizon forecasts for The Net...
This study investigates Bitcoin price forecasting using integrated sentiment analysis and gradient boosting within digital financial ecosystems. Two XGBoost models were developed using sentiment scores derived from Bitcoin news (2021–2024) and...
This paper presents a comprehensive real-time forecasting and mapping cycle of a regional flood event, encompassing quantitative precipitation forecasting, runoff production and routing, and inundation mapping. The objective of this study is to highl...
Carbon markets play a pivotal role in achieving carbon peaking targets, with accurate price forecasting being essential for effective policymaking and corporate decision making. This study develops an integrated forecasting framework, combining an ov...
The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglect...
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...
Ozone monitoring in laboratory environments is essential for ensuring personnel safety and maintaining stable experimental conditions, particularly in enclosed facilities where ozone may accumulate during high-energy radiation operations. This study...
The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is e...
The increasing penetration of renewable energy, and wind power in particular, requires accurate short-term forecasting to ensure grid stability, reduce operational uncertainty, and facilitate large-scale integration of intermittent resources. This st...
Energy forecasting in smart buildings is a critical aspect of optimizing energy consumption and improving sustainability in modern urban environments. This systematic literature review aims to provide a comprehensive analysis of the current research...
Accurate joint forecasting of electricity, cooling, heating, and gas loads is essential to the coordinated operation of integrated energy systems. However, multivariate energy load sequences exhibit strong cross-carrier coupling, non-stationarity, an...
A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functi...
Forecasting stock prices remains a central challenge in financial modelling, as markets are influenced by market sentiment, firm-level fundamentals and complex interactions between macroeconomic and microeconomic factors, for example. This study eval...
Accurate short-term forecasting of photovoltaic power generation is vital for the operational stability of isolated energy systems, especially in regions with increasing renewable energy penetration. This study presents a novel AI-based forecasting f...
Integrated enstrophy (IE) is the square of vorticity integrated over an entire hemisphere at a particular level in the atmosphere. Previous work has shown this quantity is correlated to the positive Lyapunov Exponent for hemispheric flow, and as such...
This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency and magnitude forecasting of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA)...
This paper develops a Sustainable Artificial Intelligence-Driven Wind Power Forecasting System (SAI-WPFS) to enhance the integration of renewable energy while minimizing the environmental footprint of deep learning computations. Although deep learnin...
This paper presents a comprehensive review of artificial intelligence (AI) applications in photovoltaic-integrated buildings, focusing on energy forecasting, advanced control strategies, and pathways toward net-zero energy performance. Net-zero energ...
Long-term streamflow forecast is of great significance for water resource application and management. However, accurate monthly streamflow forecasting is challenging due to its non-stationarity and uncertainty. Time series analysis methods have been...
Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exh...
Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasti...
Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey’s natural gas consumption increased as well in parallel with the world‘s over the last decad...
Day-ahead dispatch of integrated energy systems (IESs) is commonly solved by mixed-integer linear programming when objectives, constraints, and forecasts are fully specified. In practice, however, operators often express temporary preferences in natu...
Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regr...
Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Ga...
Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation fo...
The study introduces the Hybrid Anomaly-Filtered Spatio-Temporal Representation Network (HASTRN) to achieve accurate forecasting of short-term and day-ahead energy demand by utilizing smart-meter, solar PV, and weather-integrated data. Energy demand...
The article develops the author’s methodology for assessing the rates of socio-economic development and their forecasting in the Russian Federation, which makes it possible to consider factors with heterogeneous metrics. For this, an index anal...
With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial f...
The growing interest in solar energy stems from its potential to reduce greenhouse gas emissions. Global horizontal irradiance (GHI) is a crucial determinant of the productivity of solar photovoltaic (PV) systems. Consequently, accurate GHI forecasti...
Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Direc...
Building-Integrated and Building-Attached Photovoltaic (BIPV/BAPV) systems are increasingly being adopted in metropolitan areas worldwide, driven by international commitments to reduce greenhouse gas emissions and the declining cost of PV technology....
Renewable energy producers in coupled electricity and carbon markets face multi-dimensional uncertainties with cross-domain correlations. This paper proposes T-CMRS, an integrated forecasting optimization system for wind-solar-storage portfolios enha...
Accurate forecasting of high-resolution particulate matter 2.5 (PM2.5) levels is essential for the development of public health policy. However, datasets used for this purpose often contain missing observations. This study presents a two-stage approa...
Accurate daily runoff forecasting is essential for flood control and water resource management, yet existing models struggle with the seasonal non-stationarity and inter-basin variability of runoff sequences. This paper proposes a Season-Aware Ensemb...
The forecast of carbon trading price is crucial to both sellers and purchasers; multi-scale integration models have been used widely in this process. However, these multi-scale models ignore the feature reconstruction process as well as the residual...
Bayesian methods have revolutionised hydrological modelling by providing a framework for managing uncertainty, improving model calibration, and enabling more accurate predictions. This paper reviews the evolution of Bayesian methods in hydrology, fro...
This study presents an enhanced hybrid TLBO–ANN model for daily photovoltaic (PV) power generation prediction. By combining the strong nonlinear modeling capacity of Artificial Neural Networks (ANN) with the robust optimization capability of th...
This study introduces a system that merges AI with low-power IoT (Internet Of Things) technology to enhance environmental monitoring, with a specific focus on accurately predicting forest fires through time series analysis. Utilizing affordable senso...
Deep coalbed methane (CBM) reservoirs exhibit ultralow permeability, high in situ stress, and pronounced stress sensitivity. The resulting feedback between reservoir deliverability, wellbore liquid transport, and surface choking cannot be represented...
Streptococcus pneumonia is the primary cause of morbidity and mortality in infants and children globally. Invasive pneumococcal disease (IPD) incidence is affected by various risk factors such as age and comorbidities. Additionally, this bacterium is...
18 October 2022
In 2020, with a substantial decline in tourist arrivals slightly before the time of COVID-19, the innovative econometric approach predicted possible responses between the spread of human microbes (bacteria/viruses) and tourist arrivals. The article d...
The present study reviews leachate models useful for proactive and rehab actions to safeguard surface and subsurface soft water, which have become even more scarce. Integrated management plans of water basins are of crucial importance since intensive...
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