Bindu Vadlamudi¹, Subhojit Dawn^1,*, Ishwarya Devarakonda¹, Sri Hari Priya Lanka¹, Sujan Turaka¹, Taha Selim Ustun^2,*

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.083555 - 06 August 2026

Abstract Agricultural irrigation consumes a large share of electricity in rural areas, creating predictable peak conditions on distribution systems that can lead to grid instability and unreliability. Classic load-forecasting and scheduling methods are time-consuming and unable to respond rapidly to fluctuating irrigation demand. Additionally, most traditional methods require a stable internet connection to function and therefore cannot readily adapt to seasonal changes or crop-specific irrigation requirements. This creates inefficiencies in energy consumption and inconsistencies in water delivery to consumers. To reduce these drawbacks, this research proposes a framework for irrigation forecasting and dynamic scheduling for agricultural… More >

Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Rana Muhammad Adnan Ikram¹, Jing-Cheng Han^1,*, Ahmed A. Ewees², Mo Wang³, Ozgur Kisi^4,5,6,*, Salim Heddam⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083313 - 27 July 2026

Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >

Mubariz Khan¹, Hafeez Ur Rehman Siddiqui², Adil Ali Saleem², Muhammad Amjad Raza^2,3, Lázaro Javier Hernández Rodríguez^4,5,6,7, Pablo Herrero García^4,8,9, Isabel de la Torre Díez^10,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084269 - 23 July 2026

Abstract Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE,… More >

Yasemin Onal^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082593 - 23 July 2026

Abstract Photovoltaic (PV) power generation exhibits considerable sensitivity to both weather variability and fluctuations in solar irradiance. Consequently, precise forecasting of PV power is crucial for ensuring grid reliability, load balancing, and the effective functioning of energy markets within a grid-connected solar plant. Conventional forecasting methodologies frequently prove inadequate in accurately capturing the nonlinear and intricate temporal patterns present within PV datasets. To address these shortcomings, this research presents a hybrid short-term PV power forecasting model. This model integrates Neighborhood Component Analysis (NCA) for dimensionality reduction with a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) framework.… More >

Nusrat Yasmin Nadia¹, Md Habibul Arif², Habibor Rahman Rabby³, Md Iftekhar Monzur Tanvir¹, Md Jakir Hossen^4,*, M. F. Mridha⁵

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.074236 - 23 July 2026

Abstract Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand–Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)–based demand forecasting module with a mixed-integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost More >

Chuan Lin^*, Weixian Chen, Guangtao Hao^*

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.080605 - 12 July 2026

Abstract Accurate building electricity load forecasting (BELF) can provide a regulatory basis for building energy management systems and promote the transition of buildings toward low-carbon and intelligent operation modes. However, building electricity load is influenced by historical loads, as well as outside environmental conditions such as humidity and temperature, which reduces the prediction accuracy of models. To tackle these challenges, this study presents a BELF model, which consists of a modal component grouping approach, grouped feature attention mechanism, and multi-scale residual depthwise convolution memory module. First, the modal component grouping method analyzes building electricity load in… More >

Mohamed R. Aboelmagd^1,*, Ali Selim^1,2,*, Mamdouh Abdel-Akher¹

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.079171 - 12 July 2026

Abstract To enable the integration of photovoltaic (PV) power into electrical grids, accurate predictions are vital. This study applies the Multistage Attention Neural Ordinary Differential Equation (MANODE) model, which combines Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCN), and a two-stage attention mechanism to capture complex spatiotemporal patterns for PV power forecasting. The improved MANODE model is evaluated on three real-world datasets from PV stations in Egypt. Each dataset contains 12 feature parameters and spans an entire year. Comprehensive comparisons are conducted between the improved MANODE model and other neural network models, including one-layer and More >

Mingxuan Ji¹, Jing Gao^1,*, Dantian Zhong¹, Yingqi Xu¹, Shuxiang Yang¹, Zhongxiao Du¹, Yingming Liu²

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.074643 - 12 July 2026

Abstract The intermittency of wind power poses severe challenges to the safe and stable operation of power grids, while conventional forecasting models are deficient in prediction accuracy and adaptability to variable weather conditions. To address these issues, this study proposes an adaptive short-term wind power forecasting model integrating affinity propagation (AP) clustering and a black-winged kite algorithm (BKA)-optimized temporal convolutional network-bidirectional long short-term memory (TCN-BiLSTM) hybrid architecture. First, mutual information was employed to screen key meteorological features, and AP clustering categorized historical data into six distinct weather scenarios. A scenario-specific TCN-BiLSTM model was then constructed for… More >

Nantian Huang^*, Jingyuan Zhang, Shicheng Ren, Hao Zhang, Bingling Li, Yaoyao Wang

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.072993 - 12 July 2026

Abstract User electricity consumption behavior analysis and multi-load forecasting in integrated energy systems are crucial for system operation and scheduling. Traditional user electricity consumption behavior analysis fails to adequately incorporate meteorological factors, limiting the accuracy of characterizing user electricity consumption patterns. Traditional multi-load forecasting models do not consider the differentiated coupling relationships with meteorological factors across different seasons, which restricts the improvement of forecasting accuracy. To address the above issues, a method integrating data cleaning and meteorological correlation for electricity consumption behavior and multi-dimensional forecasting analysis is proposed. First, the Akima interpolation method is used to… More >