Search Results (683)

With the deepening of power market reform and the increasingly fierce competition in the power market, the accurate prediction of electricity price has become an important demand for power market participants to make scientific decisions, optimize resource allocation, and reduce risks. Electricity price forecast can provide a key reference for the power market, help market participants make wise decisions, promote competition and efficient operation and cope with complex market fluctuations, provide a scientific basis for various entities to optimize resource allocation, reduce risks and improve benefits, and promote the sustainable development of the power industry. This study presents a dynamic retail price prediction method for smart grid based on the Stackelberg game model. Firstly, the correlation test is used to verify the strong correlation between electric load and electricity price. Secondly, the parameters of the Stackelberg model are determined, and the load and electricity price are tested using the white noise test. Finally, by comparing the BP neural network model and quantifying the model parameters, the superiority of the model is verified. The results show that the Stackelberg game model has higher prediction accuracy than the BP neural network model in electricity price prediction. Full article

As power demand increases and the scale of power grids expands, accurately predicting transmission line temperatures is becoming essential for ensuring the stability and security of power systems. Traditional physical and statistical models struggle with complex multivariate time series, often failing to balance short-term fluctuations with long-term dependencies, and their prediction accuracy and adaptability remain limited. To address these challenges, this paper proposes a deep learning model architecture based on the Dynamic Adaptive Artificial Hummingbird Algorithm (DA-AHA), named the DA-AHA-CNN-LSTM-TPA (DA-AHA-CLT). The model integrates convolutional neural networks (CNNs) for local feature extraction, long short-term memory (LSTM) networks for temporal modeling, and temporal pattern attention mechanisms (TPA) for dynamic feature weighting, while the DA-AHA optimizes hyperparameters to enhance prediction accuracy and stability. The traditional artificial hummingbird algorithm (AHA) is further improved by introducing dynamic step-size adjustment, greedy local search, and grouped parallel search mechanisms to balance global exploration and local exploitation. Our experimental results demonstrate that the DA-AHA-CLT model achieves a coefficient of determination (R²) of 0.987, a root-mean-square error (RMSE) of 0.023, a mean absolute error (MAE) of 0.018, and a median absolute error (MedAE) of 0.011, outperforming traditional models such as CNN-LSTM and LSTM-TPA. These findings confirm that the DA-AHA-CLT model effectively captures the complex dynamic characteristics of transmission line temperatures, offering superior performance and robustness in full-time-step prediction tasks, and highlight its potential for solving challenging multivariate time-series forecasting problems in power systems. Full article

Accurately predicting travel mode choice is crucial for effective transportation planning and policymaking. While traditional approaches rely on discrete choice models, recent advancements in machine learning offer promising alternatives. This study proposes a novel convolutional neural network (CNN) architecture optimized using orthogonal experimental design to predict travel mode choice. Using the SwissMetro dataset, which represents a specific intercity travel context in Switzerland, we evaluate our CNN model’s performance and compare it with traditional machine learning algorithms and previous studies. The key innovations of our study include: (1) an optimized CNN architecture designed to capture complex patterns in travel behavior data, and (2) the application of orthogonal experimental design to efficiently identify optimal hyperparameter settings. The results demonstrate that the proposed CNN model significantly outperforms logit models, support vector machines, random forests, gradient boosting, and even state-of-the-art techniques combining discrete choice models with neural networks. The optimized CNN achieves a remarkable 95% accuracy, surpassing the best-performing benchmarks by 14–25%. The proposed methodology offers a powerful tool for understanding travel behavior, improving travel demand forecasting, and informing transportation planning decisions. Our findings contribute to the growing body of literature on machine learning applications in transportation and pave the way for further advancements in this field. Full article

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours. Full article

The energy sector plays a vital role in driving environmental and social advancements. Accurately predicting energy demand across various time frames offers numerous benefits, such as facilitating a sustainable transition and planning of energy resources. This research focuses on predicting energy consumption using three individual models: Prophet, eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM). Additionally, it proposes an ensemble model that combines the predictions from all three to enhance overall accuracy. This approach aims to leverage the strengths of each model for better prediction performance. We examine the accuracy of an ensemble model using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) through means of resource allocation. The research investigates the use of real data from smart meters gathered from 5567 London residences as part of the UK Power Networks-led Low Carbon London project from the London Datastore. The performance of each individual model was recorded as follows: 62.96% for the Prophet model, 70.37% for LSTM, and 66.66% for XGBoost. In contrast, the proposed ensemble model, which combines LSTM, Prophet, and XGBoost, achieved an impressive accuracy of 81.48%, surpassing the individual models. The findings of this study indicate that the proposed model enhances energy efficiency and supports the transition towards a sustainable energy future. Consequently, it can accurately forecast the maximum loads of distribution networks for London households. In addition, this work contributes to the improvement of load forecasting for distribution networks, which can guide higher authorities in developing sustainable energy consumption plans. Full article

Conventional weather forecasting relies on numerical weather prediction (NWP), which solves atmospheric equations using numerical methods. The Korea Meteorological Administration (KMA) adopted the Met Office Global Seasonal Forecasting System version 6 (GloSea6) NWP model from the UK and runs it on a supercomputer. However, due to high task demands, the limited resources of the supercomputer have caused job queue delays. To address this, the KMA developed a low-resolution version, Low GloSea6, for smaller-scale servers at universities and research institutions. Despite its ability to run on less powerful servers, Low GloSea6 still requires significant computational resources like those of high-performance computing (HPC) clusters. We integrated deep learning with Low GloSea6 to reduce execution time and improve meteorological research efficiency. Through profiling, we confirmed that deep learning models can be integrated without altering the original configuration of Low GloSea6 or complicating physical interpretation. The profiling identified “tri_sor.F90” as the main CPU time hotspot. By combining the biconjugate gradient stabilized (BiCGStab) method, used for solving the Helmholtz problem, with a deep learning model, we reduced unnecessary hotspot calls, shortening execution time. We also propose a convolutional block attention module-based Half-UNet (CH-UNet), a lightweight 3D-based U-Net architecture, for faster deep-learning computations. In experiments, CH-UNet showed 10.24% lower RMSE than Half-UNet, which has fewer FLOPs. Integrating CH-UNet into Low GloSea6 reduced execution time by up to 71 s per timestep, averaging a 2.6% reduction compared to the original Low GloSea6, and 6.8% compared to using Half-UNet. This demonstrates that CH-UNet, with balanced FLOPs and high predictive accuracy, offers more significant execution time reductions than models with fewer FLOPs. Full article

This paper explores how AI drives GCC sector retail towards the fulfillment of the UN SDGs. Analyzing a survey conducted on 410 retail executives, using PLS-SEM, this study underlines the role of AI in promoting operational efficiency, waste reduction, and consumer engagement with greener products. Key highlights include that AI-enabled marketing strategies improve the adoption of sustainable practices among consumers; AI-powered smart distribution channels enhance supply chain efficiency, reduce carbon emissions, and optimize logistics. For a retailer, practical applications of AI include the use of AI in demand forecasting to potentially reduce waste, personalized marketing to efficiently promote sustainable products, and deploying smart systems that reduce energy consumption. While these benefits are real, data privacy and algorithmic bias remain valid concerns, thus underlining the need for ethics and transparency in the practice of AI. The following study provides actionable insights for GCC retailers on how to align AI adoption with sustainability goals, fostering competitive advantages and environmental responsibility. Full article

The increased share of new energy sources in Northeast China’s power mix has strained grid stability. Energy storage technologies are essential for maintaining grid stability by addressing peak shaving and frequency regulation challenges. However, a clear quantitative assessment of the region’s energy storage needs is lacking, leading to weak grid stability and limited growth potential. This paper analyzes power supply data from Northeast China and models the stochastic characteristics of new energy generation. A joint optimization model for energy storage and thermal power is developed to optimize power allocation for peak shaving and frequency regulation at minimal cost. The empirical distribution method quantifies the relationship between storage power, capacity, and confidence levels, providing insights into the region’s future energy storage demands. The study finds that under 10 typical scenarios, the demand for peaking power at a 15 min scale is ≤500 MW, and the demand for frequency regulation at a 1 min scale is ≤1000 MW. At the 90% confidence level, the required capacity for new energy storage for peak shaving and frequency regulation is 424.13 MWh and 197.65 MWh, respectively. The required power for peak shaving and frequency regulation is 247.88 MW and 527.33 MW, respectively. The durations of peak shaving and frequency regulation are 1.71 h and 0.38 h. It also forecasts the energy storage capacity in the northeast region from 2025 to 2030 under the 5% annual incremental new energy penetration scenario. These findings provide theoretical support for energy storage policies in Northeast China during the 14th Five-Year Plan and practical guidance for accelerating energy storage industrialization. Full article

Corporate budget planning involves forecasting expenses and revenues to support strategic goals, resource allocation, and supply chain coordination. Regular updates to forecasts and collaboration across organizational levels ensure adaptability to changing business conditions. Long-term sales forecasts form the foundation for budgeting, guiding resource allocation and enhancing financial efficiency. The budgeting process in organizations is complex and requires data from various operational areas, which is collected over a representative period. Key inputs include quantitative sales data, direct costs indirect costs, and historical revenues and profitability, which are often sourced from ERP systems. While ERP systems typically provide tools for basic budgeting, they lack advanced capabilities for forecasting and simulation. We proposed a solution, which includes dynamic demand forecasting based on time series methods such as Build-in method in Power BI (which is ETS—exponential smoothing), linear regression, XGBoost, ARIMA and flexible product groupings, which are simulations for cost changes. The case study concerns a manufacturing company in the mass customization industry. The solution is designed to be intuitive and easily implemented in the business. Full article

The limited nature of fossil resources and their unsustainable characteristics have led to increased interest in renewable sources. However, significant work remains to be carried out to fully integrate these systems into existing power distribution networks, both technically and legally. While reliability holds great potential for improving energy production sustainability, the dependence of solar energy production plants on weather conditions can complicate the realization of consistent production without incurring high storage costs. Therefore, the accurate prediction of solar power production is vital for efficient grid management and energy trading. Machine learning models have emerged as a prospective solution, as they are able to handle immense datasets and model complex patterns within the data. This work explores the use of metaheuristic optimization techniques for optimizing recurrent forecasting models to predict power production from solar substations. Additionally, a modified metaheuristic optimizer is introduced to meet the demanding requirements of optimization. Simulations, along with a rigid comparative analysis with other contemporary metaheuristics, are also conducted on a real-world dataset, with the best models achieving a mean squared error (MSE) of just 0.000935 volts and 0.007011 volts on the two datasets, suggesting viability for real-world usage. The best-performing models are further examined for their applicability in embedded tiny machine learning (TinyML) applications. The discussion provided in this manuscript also includes the legal framework for renewable energy forecasting, its integration, and the policy implications of establishing a decentralized and cost-effective forecasting system. Full article

Due to the supply problems of fossil-based energy sources, the tendency towards alternative energy sources is relatively high. For this reason, the use of solar energy systems is increasing today. This study combines experimental data and machine learning algorithms to evaluate the energy performance of four different photovoltaic (PV) panel designs (monocrystalline, polycrystalline, flexible, and transparent) under harsh environmental conditions on Horseshoe Island (Antarctica). In this research, the effects of environmental factors, such as solar radiation, temperature, humidity, and wind speed, on the panels were analyzed. Electrical power output of the PV panels are analyzed using six machine learning models. Random forest (RF) and CatBoost (CB) models showed the highest accuracy and reliability among these models. According to the experimental results, Monocrystalline PV provided the highest electrical power (20.5 Watts on average), and Flexible PV provided the highest energy efficiency (19.67%). However, Flexible PV was observed to have higher surface temperatures compared to the other panel types. Furthermore, using Monocrystalline PV resulted in an average reduction of 4.1 tons of CO₂ emissions per year, demonstrating the positive environmental impact of renewable energy systems. Thanks to this study, renewable energy research for temporary stations in Antarctica will focus on explainable and interpretable artificial intelligence models that will provide an understanding of the factors affecting the energy performance of PV panels. The research results will be an important guide for optimizing energy consumption, management, and demand forecasting in temporary research stations in Antarctica. Full article

The integration of Full Electric Vehicles (FEVs) into the smart city ecosystem is an essential step towards achieving sustainable urban mobility. This study presents a comprehensive mobility network model designed to predict and optimize the energy supply for FEVs within smart cities. The model integrates advanced components such as a Charge Station Control Center (CSCC), smart charging infrastructure, and a dynamic user interface. Important aspects include analyzing power consumption, forecasting urban energy demand, and monitoring the State of Charge (SoC) of FEV batteries using innovative algorithms validated through real-world applications in Valencia (Spain) and Ljubljana (Slovenia). Results indicate high accuracies in SoC tracking (error < 0.05%) and energy demand forecasting (MSE ~6 × 10⁻⁴), demonstrating the model’s reliability and adaptability across diverse urban environments. This research contributes to the development of resilient, efficient, and sustainable smart city frameworks, emphasizing real-time data-driven decision-making in energy and mobility management. Full article

The Integrated Resource Plan (IRP) 2019 outlines South Africa’s goal of achieving a diverse and sustainable energy mix. To achieve this, innovative methods must be found to integrate renewable energy sources while preserving grid stability. Virtual Power Plants (VPPs), which combine dispersed energy resources like solar photovoltaic (PV), wind, and battery storage into a single, intelligent system, are one such approach. This study provides a thorough analysis of the best way to integrate VPPs into South Africa’s national grid, highlighting the associated operational, regulatory, and technological challenges. In order to optimize VPP efficiency, this research looks at a number of key areas, such as enhanced renewable energy forecasting, energy management systems (EMSs), and distributed energy resource (DER) integration. Additionally, it examines how VPPs help demand-side management, reduce intermittency in renewable energy sources, and improve grid flexibility. In addition, this paper analyzes the market and regulatory structures required to permit VPP participation in energy markets and guarantee a smooth transition to a decentralized energy environment. This paper highlights the crucial role VPPs could play in reaching the nation’s renewable energy targets, lowering dependency on fossil fuels, and enhancing energy access. Through this review, this paper offers insights into the technological viability and strategic benefits of VPP implementation in South Africa. The findings highlight that for VPPs to successfully integrate into South Africa’s energy landscape, it will be necessary to overcome technological, regulatory, and market-related barriers. Full article

Accurate residential load forecasting is crucial for the stable operation of the energy internet, which plays a significant role in advancing sustainable development. As the construction of the energy internet progresses, the proportion of residential electricity consumption in end-use energy consumption is increasing, the peak load on the grid is growing year on year, and seasonal and regional peak power supply tensions, mainly for household electricity consumption, grow into common problems across countries. Residential load forecasting can assist utility companies in determining effective electricity pricing structures and demand response operations, thereby improving renewable energy utilization efficiency and reducing the share of thermal power generation. However, due to the randomness and uncertainty of user load data, forecasting residential load remains challenging. According to prior research, the accuracy of residential load forecasting using machine learning and deep learning methods still has room for improvement. This paper proposes an improved load-forecasting model based on a time-localized attention (TLA) mechanism integrated with LSTM, named TLA-LSTM. The model is composed of a full-text regression network, a date-attention network, and a time-point attention network. The full-text regression network consists of a traditional LSTM, while the date-attention and time-point attention networks are based on a local attention model constructed with CNN and LSTM. Experimental results on real-world datasets show that compared to standard LSTM models, the proposed method improves R² by 14.2%, reduces MSE by 15.2%, and decreases RMSE by 8.5%. These enhancements demonstrate the robustness and efficiency of the TLA-LSTM model in load forecasting tasks, effectively addressing the limitations of traditional LSTM models in focusing on specific dates and time-points in user load data. Full article

Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporates time-series power consumption data and considers occupancy. A real-world dataset containing the per-minute electricity consumption of various appliances in an office building in Houston, TX, USA, is utilized. The proposed machine learning models forecast future energy consumption based on hourly, 3-hourly, daily, and quarterly predictions for individual appliances and total energy usage. The model’s performance is evaluated using the following three metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrate the superiority of the proposed system. Full article