Methods of Forecasting Electric Energy Consumption: A Literature Review

Energies, Journal Year: 2022, Volume and Issue: 15(23), P. 8919 - 8919

Published: Nov. 25, 2022

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on means methods planning production. Forecasting one tools since availability accurate forecast a mechanism for increasing validity management decisions. This study provides overview used to predict supply requirements different objects. The have been reviewed analytically, taking into account classification according anticipation period. In this way, in operative, short-term, medium-term, long-term forecasting considered. Both classical modern identified when electric energy consumption. Classical are based theory regression statistical analysis (regression, autoregressive models); probabilistic use deep-machine-learning algorithms, rank methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due need take specifics each subject area characterizing facility obtain reliable results, power modeling remains task despite wide variety other methods. review was conducted with assessment following criteria: labor intensity, initial data set, scope application, accuracy method, possibility application horizons. above period allows highlights fact that predicting time intervals, same often used. Therefore, it worth emphasizing importance classifying over horizon not differentiate but consider type (operative, long-term).

Language: Английский

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Forecasting solar energy production: A comparative study of machine learning algorithms

Energy Reports, Journal Year: 2023, Volume and Issue: 10, P. 1004 - 1012

Published: Aug. 4, 2023

The use of solar energy has been rapidly expanding as a clean and renewable source, with the installation photovoltaic panels on homes, businesses, large-scale farms. increasing demand for sustainable sources pushed growth industry, well advancements in technology, making more efficient cost-effective. implementation not only reduces our reliance non-renewable fossil fuels but also helps to mitigate effects climate change by reducing carbon emissions. This paper presents complete comparative study production forecasting Morocco using six machine learning (ML) algorithms : Support Vector Regression (SVR), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Generalized Additive Model (GAM) Extreme Gradient Boosting (XGBOOST), based Solar Power Plant daily data installed Benguerir city between January December 2022. models were trained, tested, then evaluated. In order assess performance four metrics used this study, namely root mean squared error (RMSE), absolute (MAE), scaled (MASE)and R-squared (R2). reveals ANN be most effective predictive model similar cases lowest value RMSE, MSAE highest R-squared, which are accepted one important criteria model. findings validate effectiveness algorithm offer appropriate parameters achieving best results predicting production. By identifying optimal configuration algorithm, we provide valuable insights that can directly applied real-world applications, thereby enhancing optimization systems contributing future, particularly integration these an edge device maintenance power plants.

Language: Английский

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Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Energies, Journal Year: 2023, Volume and Issue: 16(10), P. 4025 - 4025

Published: May 11, 2023

The use of machine learning and data-driven methods for predictive analysis power systems offers the potential to accurately predict manage behavior these by utilizing large volumes data generated from various sources. These have gained significant attention in recent years due their ability handle amounts make accurate predictions. importance particular momentum with transformation that traditional system underwent as they are morphing into smart grids future. transition towards embed high-renewables electricity is challenging, generation renewable sources intermittent fluctuates weather conditions. This facilitated Internet Energy (IoE) refers integration advanced digital technologies such Things (IoT), blockchain, artificial intelligence (AI) systems. It has been further enhanced digitalization caused COVID-19 pandemic also affected energy sector. Our review paper explores prospects challenges using provides an overview ways which constructing can be applied order them more efficient. begins description role operations. Next, discusses systems, including benefits limitations. In addition, reviews existing literature on this topic highlights used Furthermore, it identifies opportunities associated methods, quality availability, discussed. Finally, concludes a discussion recommendations research application future grid-driven powered IoE.

Language: Английский

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Generic Multi-Layered Digital-Twin-Framework-Enabled Asset Lifecycle Management for the Sustainable Mining Industry

Sustainability, Journal Year: 2023, Volume and Issue: 15(4), P. 3470 - 3470

Published: Feb. 14, 2023

In the era of digitalization, many technologies are evolving, namely, Internet Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety sectors, including mining industry. The use DT industry is driven by its potential to improve efficiency, productivity, sustainability monitoring performance, simulating results, predicting errors yield. Additionally, increasing demand for individualized products highlights need effective management entire product lifecycle, from design development, modeling, simulating, prototyping, maintenance troubleshooting, commissioning, targeting market, use, end-of-life. However, problem be overcome how successfully integrate into business. This paper intends shed light on state art case studies focusing concept, design, development. reference architecture model Industry 4.0 value-lifecycle-management-enabled also discussed, proposition multi-layered framework explained inspire future studies.

Language: Английский

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Smart home energy management systems: Research challenges and survey

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 92, P. 117 - 170

Published: March 5, 2024

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage expected increase by more than 40% 20 years. Therefore, compensate for demand requirements, proper planning strategies are needed improve home management systems (HEMs). One crucial aspects HEMS load forecasting scheduling utilization. Energy depend heavily on precise scheduling. Considering this scenario, article was divided into two parts. Firstly, gives thorough analysis models HEMs with primary goal determining whichever model most appropriate given situation. Moreover, optimal utilization HEMs, current literature has discussed number optimization approaches. secondly article, these approaches be examined thoroughly develop effective operating make wise judgments regarding techniques HEMs. Finally, paper also presents future technical advancements research gaps how they affect activities near future.

Language: Английский

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Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network

Sustainability, Journal Year: 2022, Volume and Issue: 14(20), P. 13022 - 13022

Published: Oct. 12, 2022

Accurate prediction of photovoltaic power is great significance to the safe operation grids. In order improve accuracy, a similar day clustering convolutional neural network (CNN)–informer model was proposed predict power. Based on correlation analysis, it determined that global horizontal radiation meteorological factor had greatest impact power, and dataset divided into four categories according between factors fluctuation characteristics; then, CNN used extract feature information trends different subsets, features output by were fused input informer model. The establish temporal relationship historical data, final generation result obtained. experimental results show CNN–informer method has high accuracy stability in outperforms other deep learning methods.

Language: Английский

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Smart Energy Management: A Comparative Study of Energy Consumption Forecasting Algorithms for an Experimental Open-Pit Mine

Energies, Journal Year: 2022, Volume and Issue: 15(13), P. 4569 - 4569

Published: June 22, 2022

The mining industry’s increased energy consumption has resulted in a slew of climate-related effects on the environment, many which have direct implications for humanity’s survival. forecast mine site use is one low-cost approaches conservation. Accurate predictions do indeed assist us better understanding source high and aid making early decisions by setting expectations. Machine Learning (ML) methods are known to be best approach achieving desired results prediction tasks this area. As result, machine learning been used several research involving operational residential buildings. Only few research, however, investigated feasibility algorithms predicting open-pit mines. To close gap, work provides an application RapidMiner tool time series using real-time data obtained from smart grid placed experimental mine. This study compares performance four daily consumption: Artificial Neural Network (ANN), Support Vector (SVM), Decision Tree (DT), Random Forest (RF). models were trained, tested, then evaluated. In order assess models’ metrics study, namely correlation (R), mean absolute error (MAE), root squared (RMSE), relative (RRSE). reveals RF most effective predictive model forecasting similar cases.

Language: Английский

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An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

Energies, Journal Year: 2022, Volume and Issue: 15(19), P. 7217 - 7217

Published: Oct. 1, 2022

After the massive integration of distributed energy resources, storage systems and charging stations electric vehicles, it has become very difficult to implement an efficient grid management system regarding unmanageable behavior power flow within grid, which can cause many critical problems in different stages, typically substations, such as failures, blackouts, transformer explosions. However, current digital transition toward Energy 4.0 Smart Grids allows smart solutions substations by integrating sensors implementing new control monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for transformers, diagnostic algorithms, Health Index, life-loss estimation approaches. gathering datasets, this presents exhaustive algorithm comparative study select best fit models. developed architecture prognostic (PHM) health interaction between evolutionary support vector machine, random forest, k-nearest neighbor, linear regression-based models connected online transformer; these interactions are calculating important key performance indicators related alarms that gives decisions on load management, factor control, maintenance schedule planning.

Language: Английский

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An approach towards demand response optimization at the edge in smart energy systems using local clouds

Smart Energy, Journal Year: 2023, Volume and Issue: 12, P. 100123 - 100123

Published: Oct. 24, 2023

The fourth and fifth industrial revolutions (Industry 4.0 Industry 5.0) have driven significant advances in digitalization integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) emerged as crucial tools addressing climate change, integrating smart grids homes/buildings to improve energy infrastructure. To achieve a robust SES, stakeholders must collaborate efficiently through an management framework based on Internet Things (IoT). Demand Response (DR) is key balancing demands costs. This research proposes edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are Service-Oriented Architecture that promotes stakeholders. novel solution guarantees secure, low-latency communication among various home IoT technologies. study also introduces theoretical employs AI at edge create environment profiles buildings, optimizing DR ensuring human comfort. By focusing room-level optimization, aims overall efficiency SESs foster practices.

Language: Английский

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Strategic Load Management: Enhancing Eco-Efficiency in Mining Operations Through Automated Technologies

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 102890 - 102890

Published: Sept. 1, 2024

Language: Английский

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