Search Results (1,211)

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

The energy transition, driven by the global shift toward renewable and electrification, necessitates accurate and efficient prediction of electrical load profiles to quantify energy consumption. Therefore, the systematic literature review (SLR), followed by PRISMA guidelines, synthesizes hybrid architectures for sequential electrical load profiles, aiming to span statistical techniques, machine learning (ML), and deep learning (DL) strategies for optimizing performance and practical viability. The findings reveal a dominant trend towards complex hybrid models leveraging the combined strengths of DL architectures such as long short-term memory (LSTM) and optimization algorithms such as genetic algorithm and Particle Swarm Optimization (PSO) to capture non-linear relationships. Thus, hybrid models achieve superior performance by synergistically integrating components such as Convolutional Neural Network (CNN) for feature extraction and LSTMs for temporal modeling with feature selection algorithms, which collectively capture local trends, cross-correlations, and long-term dependencies in the data. A crucial challenge identified is the lack of an established framework to manage adaptable output lengths in dynamic neural network forecasting. Addressing this, we propose the first explicit idea of decoupling output length predictions from the core signal prediction task. A key finding is that while models, particularly optimization-tuned hybrid architectures, have demonstrated quantitative superiority over conventional shallow methods, their performance assessment relies heavily on statistical measures like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). However, for comprehensive performance assessment, there is a crucial need for developing tailored, application-based metrics that integrate system economics and major planning aspects to ensure reliable domain-specific validation. Full article

With the rapid advancement of artificial intelligence (AI) technology, training deep neural networks has become a core computational task that consumes significant energy in data centers. Researchers often employ various methods to estimate the energy usage of data center clusters or servers to enhance energy management and conservation efforts. However, accurately predicting the energy consumption and carbon footprint of a specific AI task throughout its entire lifecycle before execution remains challenging. In this paper, we explore the energy consumption characteristics of AI model training tasks and propose a simple yet effective method for predicting neural network training energy consumption. This approach leverages training task metadata and applies genetic programming-based symbolic regression to forecast energy consumption prior to executing training tasks, distinguishing it from time series forecasting of data center energy consumption. We have developed an AI training energy consumption environment using the A800 GPU and models from the ResNet{18, 34, 50, 101}, VGG16, MobileNet, ViT, and BERT families to collect data for experimentation and analysis. The experimental analysis of energy consumption reveals that the consumption curve exhibits waveform characteristics resembling square waves, with distinct peaks and valleys. The prediction experiments demonstrate that the proposed method performs well, achieving mean relative errors (MRE) of 2.67% for valley energy, 8.42% for valley duration, 5.16% for peak power, and 3.64% for peak duration. Our findings indicate that, within a specific data center, the energy consumption of AI training tasks follows a predictable pattern. Furthermore, our proposed method enables accurate prediction and calculation of power load before model training begins, without requiring extensive historical energy consumption data. This capability facilitates optimized energy-saving scheduling in data centers in advance, thereby advancing the vision of green AI. Full article

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

A Smart campus increasingly operates on the basis of data-driven operations, but an increasing demand for energy puts their control over costs and sustainability at risk. This study addresses the challenge of anticipating and managing energy consumption peaks in multi-campus environments by proposing a hybrid framework that combines advanced time-series forecasting models with a large language model (LLM)-driven multi-agent system. Based on the UNICON dataset, LSTM, CNN, GRU, and a combination architecture are trained and compared in terms of MAE and RMSE. The hybrid configuration achieves the greatest forecasting results by returning the minimum loss values. For the identification of critical periods, we employed a strategy based on median thresholding, which offers a categorization into low, normal, and extreme category, allowing the targeting of peak mitigation actions. We also introduce a multi-agent system based on the LLM, including the data aggregator, the forecaster, and the policy advisor, which create actionable policies informed by context. We also compare LLMs (Qwen-2.5, Gemma-2, Phi-4, Mistral, Llama-3.3) in terms of context accuracy, response relevance, semantic similarity, and retrieval/recall accuracy and fidelity, with Llama-3.3 achieving the best overall results. This framework has shown great potential, not only for energy consumption forecasting but also for developing precise policies on how to effectively manage energy consumption peaks. Full article

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R² value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). Full article

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments. Full article

Ship weather routing optimization has evolved from deterministic great-circle navigation to sophisticated frameworks that account for dynamic environmental conditions and operational constraints. This paper presents a waypoint-sequencing Model Predictive Control (MPC) approach for energy-efficient ship weather routing under forecast uncertainty. The proposed rolling horizon framework integrates neural network-based vessel performance models with ensemble weather forecasts to enable real-time route adaptation while balancing fuel efficiency, navigational safety, and path smoothness objectives. The MPC controller operates with a 6 h control horizon and 24 h prediction horizon, re-optimizing every 6 h using updated meteorological forecasts. A multi-objective cost function prioritizes fuel consumption (60%), safety considerations (30%), and trajectory smoothness (10%), with an exponential discount factor (γ = 0.95) to account for increasing forecast uncertainty. The framework discretises planned routes into waypoints and optimizes heading angles and discrete speed options (12.0, 13.5, and 14.5 knots) at each control step. Validation using 21 transatlantic voyage scenarios with real hindcast weather data demonstrates the method’s capability to propagate uncertainties through ship performance models, yielding probabilistic estimates for attainable speed, fuel consumption, and estimated time of arrival (ETA). The methodology establishes a foundation for more advanced stochastic optimization approaches while offering immediate operational value through its computational tractability and integration with existing ship decision support systems. Full article

Accurate prediction of sensor network data in critical domains such as electric power systems and traffic planning is a core task for ensuring grid stability and enhancing urban operational efficiency. Although deep learning models have achieved significant architectural advancements, their training strategy implicitly assumes that all future events are equally predictable, ignoring that the future evolution of sensor signals intertwines deterministic patterns with stochastic events and that prediction difficulty increases with temporal distance. Forcing a model to fit inherently unpredictable events with a uniform supervision may impair its ability to learn generalizable patterns. To address this, we introduce an Unpredictability Perception loss that dynamically computes a supervision weight. The computation of this weight unifies two assessment dimensions of the intrinsic unpredictability of the forecasting task. The first originates from a posterior analysis of the signal content’s randomness, while the second stems from an a priori consideration of temporal distance. The first dimension, through a complexity-aware weight derived from local spectral entropy, reduces supervision on random segments of the signal. The second dimension, through a temporal decay weight based on exponential decay, lessens supervision for distant future points. Applied to the advanced TimeMixer model, experimental results show that our approach achieves performance improvements across multiple public benchmark datasets. By matching the supervision strength to the intrinsic predictability of the signals, our proposed Unpredictability Perception loss function enhances the forecasting accuracy for sensor network data, providing a more reliable technical foundation for ensuring the stability of critical infrastructures like power grids and optimizing urban traffic systems. Full article

Unmanned aerial vehicles (UAVs) are playing increasingly important roles in military operations, disaster relief, agriculture, and communications. However, their performance is limited by energy management problems, especially in hybrid systems such as those combining fuel cells with a lithium battery. The potential of deep learning to significantly improve UAV power management is investigated in this work through adaptive forecasting and real-time optimization. We develop smart algorithms that automatically balance energy efficiency and communication performance for heterogeneous wireless networks. The simulation results demonstrate energy consumption savings, optimized flight altitudes, and spectral efficiency improvements compared to Fixed Weight and Fuzzy Logic Weight schemes. At saturated user densities, the model enables up to 42% lower energy consumption and 54% higher throughput. Moreover, predictive models based on recurrent and transformer-based deep networks allow UAVs to predict energy requirements over a variety of mission and environmental contexts, shifting from reactive approaches to proactive control. The adoption of these methods in UAV-aided beyond-5G (B5G) and future 6G network scenarios can potentially prolong endurance times and enhance mission connectivity and reliability in challenging environments. This work lays the foundation for an all-aspect framework to control and manage UAV energy in the 5G era, which takes advantage of not only deep learning but also edge computing and hybrid power systems. Deep learning is confirmed to be a keystone of sustainable, autonomous, and energy-aware UAVs operation for next-generation networks. Full article

Global warming has become a top priority on the international environmental policy agenda. The recent rise in CO₂ emissions observed in Türkiye has further emphasized the country’s critical role in addressing climate change. This study aims to estimate Türkiye’s CO₂ emissions through 2030 and identify the key socioeconomic and environmental factors driving these emissions, using multiple linear regression (MLR) and time series analysis methods. Six primary variables are examined: population, gross domestic product (GDP), CO₂ intensity, per capita energy consumption, total greenhouse gas (GHG) emissions, and forest area. This study introduces a new multivariate forecasting framework that integrates time series projections with multiple linear regression and elasticity-based sensitivity analysis, providing novel insight into the relative influence of key emission drivers compared to prior research. The results suggest that, if current policy trends persist, Türkiye’s CO₂ emissions will increase substantially by 2030. Variables such as GHG emissions, energy consumption, and population growth are found to have an increasing effect on emissions, while the limited expansion of forest areas is insufficient to offset this trend. In contrast, the negative correlation between GDP and CO₂ emissions suggests that economic growth can occur in alignment with environmental sustainability. The model’s validity is supported by a high R² (0.99) value and low error rates. The findings indicate that Türkiye must reassess its current strategies and strengthen policies targeting renewable energy, energy efficiency, and carbon sinks to achieve its climate goals. The proposed framework provides a transparent basis for climate planning and policy prioritization in Türkiye. Full article

The growing demand for energy-efficient buildings necessitates innovative approaches to reduce energy consumption during early design stages. While traditional physics-based simulations remain time- and expertise-intensive, automated machine learning (AutoML) offers a promising alternative by enabling data-driven building performance prediction with minimal human intervention. This study conducts a benchmark evaluation of AutoML’s potential in building energy applications through three objectives: (1) a literature review revealing AutoML’s nascent adoption (10 identified studies) and primary use cases (heating/cooling prediction, energy demand forecasting); (2) a benchmark comparing three AutoML frameworks (AutoGluon, H2O, Auto-sklearn) against baseline and ensemble ML models using R², RMSE, MSE, and MAE metrics; and (3) SHAP (SHapley Additive exPlanations)-based interpretability analysis. Results demonstrate AutoGluon’s superior accuracy (R² = 0.993, RMSE = 2.280 kWh/m²) in predicting energy performance, outperforming traditional methods. Key influential features, including solar heat gain coefficient (SHGC) and U-values, were identified through SHAP, offering actionable design insights. The primary novelty of this work lies in its two-step methodology: a focused review to identify pertinent AutoML frameworks, followed by a comparative benchmarking of these frameworks against traditional ML for early-stage prediction. This process substantiates AutoML’s potential to democratize energy modeling and deliver practical, interpretable workflows for architectural design. Full article

Reliable estimates of regional energy consumption are essential to planning sustainable development and achieving decarbonization; however, this information is still not available for several regions worldwide. In this work, we propose a methodological framework that uses satellite-derived Nighttime Light (NTL) imagery and machine learning to predict regional electricity consumption one year ahead. The methodology follows three stages: First, a Random Forest regression model is used to identify the relationship between NTL data and regional energy consumption. Thereafter, NTL values for the year ahead are forecasted using NTL values from previous years. Lastly, the obtained result is applied to estimate regional energy consumption from predicted NTL values for the year ahead. The country of Mexico is considered a case study to apply and validate this methodology, reproducing spatial consumption patterns with high correlation to official data ($R^2>0.85)$, thus confirming the success of this proposal. The proposed methodology demonstrates how energy demand can be estimated, even in areas of scarce information, providing a transparent and replicable approach for energy monitoring in data-limited regions. Full article

Accurate forecasting of metro energy consumption is essential for intelligent power management and sustainable urban transportation systems. However, existing studies often overlook the intrinsic properties of metro energy time series, such as strong periodicity, inter-line heterogeneity, and pronounced non-stationarity. To address this gap, this paper proposes an enhanced Informer-based framework, PatchConvFormer (PCformer). The model integrates three key innovations: (1) a channel-independent modeling mechanism that reduces interference across metro lines; (2) a patch-based temporal segmentation strategy that captures fine-grained intra-cycle energy fluctuations; and (3) a multi-scale convolution-augmented attention module that jointly models short-term variations and long-term temporal dependencies. Using real operation data from 16 metro lines in a major city in China, PCformer achieves significant improvements in forecasting accuracy (MSE = 0.043, MAE = 0.145). Compared with the strongest baseline model in each experiment (i.e., the second-best model), the MSE and MAE are reduced by approximately 41.9% and 19.8%, respectively. In addition, the model maintains strong stability and generalization across different prediction horizons and cross-line transfer experiments. The results demonstrate that PCformer effectively enhances Informer’s capability in modeling complex temporal patterns and provides a reliable technical framework for metro energy forecasting and intelligent power scheduling. Full article