Forecasting Models and Genetic Algorithms for Researching and Designing Photovoltaic Systems to Deliver Autonomous Power Supply for Residential Consumers by Ekaterina Gospodinova, Dimitar Nenov

“…The goal is to make decisions that are more valid and useful by creating a forecast model and algorithms for analyzing small PV indicators whose current values are shown by short time series and automating the processes needed for forecasting and analysis.…”
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Forecasting thermoelectric power generation through utilization of waste heat from building cooling systems based on simulation by Catur Harsito, Riyadi Muslim, Eki Rovianto, Yudi Kurniawan, Fathin Muhammad Mahdhudhu

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Modeling Sea Surface Temperature Variability with Meteorological and Water Quality Indicators Using VAR and Prophet Forecasting Models by L. Elneel, N. A. Albakri, M. S. Zitouni, S. Almansoori, H. Al-Ahmad

“…The Facebook Prophet model was applied to forecast SST and was proven capable of capturing seasonal variations and irregular spikes. …”
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Epidemiological Situation on Rickettsial Diseases and Q Fever in the Russian Federation over the Period of 2010–2023, Forecast for 2024 by S. V. Shtrek, N. V. Rudakov, S. N. Shpynov, A. I. Blokh, D. V. Trankvilevsky, N. A. Pen’evskaya, L. V. Kumpan, A. V. Sannikov

“…The aim of the review is to forecast the incidence of rickettsial infections and Q fever in the Russian Federation for 2024 based on an analysis of the epidemiological situation in 2010–2023. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. …”
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Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study by Xin Xiong, Linghui Xiang, Litao Chang, Irene XY Wu, Shuzhen Deng

“…Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). …”
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Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. by Martin Kostadinov, Eftim Zdravevski, Petre Lameski, Paulo Jorge Coelho, Biljana Stojkoska, Michael A Herzog, Vladimir Trajkovik

“…This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. …”
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MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit by Chenxi Wang, Huizhen Zhang, Shuilin Yao, Wenlong Yu, Ming Ye

“…Passenger flow forecasting plays an important role in urban rail transit (URT) management. …”
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Impact of ambient temperature on short-term maximum electrical demand through the performance of forecast models generated with Prophet by César A. Yajure-Ramírez

“…The objective of this research is to determine the impact of ambient temperature on the short-term maximum electrical demand through the performance of the forecast models, integrating into a single indicator the temperature measurements from different points of the geographical area under analysis, using as weighting factors to the proportions of regional demands with respect to total demand. …”
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Forecasting the Effectiveness of COVID-19 Vaccination Using Vector Autoregressive with an Exogenous Variable: On the Cases of COVID-19 in Indonesia by Sukono Sukono, Riza Andrian Ibrahim, Riaman Riaman, Elis Hertini, Yuyun Hidayat, Jumadil Saputra

“…This study aims to forecast the COVID-19 spread in Indonesia involving vaccination factors using vector autoregressive with exogenous variables (VARX). …”
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Forecasting the Covolatility of Coffee Arabica and Crude Oil Prices: A Multivariate GARCH Approach with High-Frequency Data by Dawit Yeshiwas, Yebelay Berelie

“…The study used weekly price data to explicitly model covolatility and employed high-frequency intraday data to assess model forecasting performance. The analysis points to the conclusion that the varying conditional correlation (VCC) model with Student’s t distributed innovation terms is the most accurate volatility forecasting model in the context of our empirical setting. …”
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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…The primary aim of this study is to forecast wind speed in the Limpopo province of South Africa to showcase the dependability and potential of wind power generation. …”
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Forecasting Daily and Monthly Reference Evapotranspiration in the Aidoghmoush Basin Using Multilayer Perceptron Coupled with Water Wave Optimization by Fatemeh Sayyahi, Saeed Farzin, Hojat Karami

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Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) by Putri Gunandra Siregar, Ilham Sahputra, Fidyatun Nisa

“…The ARIMA model is identified through ACF and PACF plot analysis, estimated, and tested before being used for forecasting. …”
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Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model by Huiming Duan, Xinping Xiao, Lingling Pei

“…The research on short-term traffic-flow forecasting is of wide interest. Its results can be applied directly to advanced traffic information systems and traffic management, providing real-time and effective traffic information. …”
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Implementing a Hybrid Quantum Neural Network for Wind Speed Forecasting: Insights from Quantum Simulator Experiences by Ying-Yi Hong, Jay Bhie D. Santos

“…As the integration of wind energy into the power system increases, accurate wind speed forecasting becomes crucial. The reliable scheduling of wind power generation heavily relies on precise wind speed forecasts. …”
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A Comparative Study of Statistical and Machine Learning Methods for Solar Irradiance Forecasting Using the Folsom PLC Dataset by Oscar Trull, Juan Carlos García-Díaz, Angel Peiró-Signes

“…The increasing penetration of photovoltaic solar energy has intensified the need for accurate production forecasting to ensure efficient grid operation. This study presents a critical comparison of traditional statistical methods and machine learning approaches for forecasting solar irradiance using the benchmark Folsom PLC dataset. …”
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Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China by Ruili Guo, Zhengdong Huang

“…The proposed PCR-based DRM provides insights for forecasting transit demand brought about by new metro lines and forecasting the consequences of land use development.…”
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Forecast Accuracy Considering Accruals Earnings Volatility (Case Study of Iran and Iraq): A Spatial Econometric Approach by Qassim Mahal Herez, Parviz Piri, Akbar Zavari Rezaei

“…Furthermore, our analysis reveals that larger and more established banks tend to have greater forecast accuracy, while higher book-to-market ratios and increased financial leverage are associated with lower accuracy. …”
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A study of Summer Rainfall on the Southern Coast of the Caspian Sea Using WRF and Satellite Numerical Forecasting Models by Donya Sadegh Nezhad, Ebrahim Fatehi, Gholam Ali Kamali, Zahra Ghassabi

“…The more accurate the rainfall forecast is, the better the flood can be predicted. …”
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