An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model DOI Creative Commons

Andreas Lenk,

Marcus Vogt, Christoph Herrmann

и другие.

Energies, Год журнала: 2024, Номер 18(1), С. 2 - 2

Опубликована: Дек. 24, 2024

The increasing share of renewable energies within energy systems leads to an increase in complexity. growing complexity is due the diversity technologies, ongoing technological innovations, and fluctuating electricity production. To continue ensure a secure, economical, needs-based supply, additional information needed efficiently control these systems. This impacts public industrial supply systems, such as vehicle factories. paper examines influencing factors applicability Temporal Fusion Transformer (TFT) model for weekly demand forecast at automobile production site. Seven different TFT models were trained demand. Six predicted electricity, heat, natural gas. Three used rolling day-ahead forecast, three entire week one step. In seventh model, was again, with target values being same model. analysis shows that forecasting method MAPE 13% already delivers good results predicting electrical prediction accuracy achieved sufficient use outcomes basis operational planning reporting. However, further improvements are still required automated system reduce procurement costs. heat gas demands show too high deviations, 62% 39% accurately predict demands, must be identified explain

Язык: Английский

Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems DOI
Hanxin Yu,

Shanlin Chen,

Yinghao Chu

и другие.

Applied Energy, Год журнала: 2024, Номер 374, С. 124007 - 124007

Опубликована: Июль 31, 2024

Язык: Английский

Процитировано

2

Feasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers DOI
Sebastian Pütz, Hadeer El Ashhab, Matthias Hertel

и другие.

Опубликована: Май 31, 2024

Язык: Английский

Процитировано

1

Digital Twins: Case Study of Energy Metaverse and Edge-Cloud Integration DOI
Ali Aghazadeh Ardebili, Angelo Martella, Cristian Martella

и другие.

2021 IEEE International Conference on Big Data (Big Data), Год журнала: 2024, Номер unknown, С. 5476 - 5485

Опубликована: Дек. 15, 2024

Язык: Английский

Процитировано

1

Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System DOI
Gargya Gokhale, Jonas Van Gompel, Bert Claessens

и другие.

Опубликована: Ноя. 3, 2023

Increasingly, homeowners opt for photovoltaic (PV) systems and/or battery storage to minimize their energy bills and maximize renewable usage. This has spurred the development of advanced control algorithms that maximally achieve those goals. However, a common challenge faced while developing such controllers is unavailability accurate forecasts household power consumption, especially shorter time resolutions (15 minutes) in data-efficient manner. In this paper, we analyze how transfer learning can help by exploiting data from multiple households improve single house's load forecasting. Specifically, train an forecasting model (a temporal fusion transformer) using different households, then finetune global on new with limited (i.e., only few days). The obtained models are used consumption next 24 hours (day-ahead) at resolution 15 minutes, intention these as Model Predictive Control. We show benefit setup versus solely individual household's data, both terms real-world data.

Язык: Английский

Процитировано

3

Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting DOI
Konstantin Hopf, Hannah Hartstang, Thorsten Staake

и другие.

Опубликована: Июнь 20, 2023

Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the supply. With global transition to distributed renewable energy sources electrification heating transportation, accurate load forecasts become even more important. While numerous empirical studies handful review articles exist, there is surprisingly little quantitative analysis literature, most notably none that identifies impact factors on forecasting performance across entirety studies. In this article, we therefore present Meta-Regression Analysis (MRA) examines influence accuracy short-term forecasts. We use data from 421 forecast models published 59 grid level (esp. individual vs. aggregated system), granularity, algorithms used seem have significant MAPE, bibliometric data, dataset sizes, prediction horizon show no effect. found LSTM approach combination neural networks with other approaches be best methods. The results help practitioners researchers make meaningful model choices. Yet, paper calls for further MRA field close blind spots research practice forecasting.

Язык: Английский

Процитировано

2

Explainable forecasting of global horizontal irradiance over multiple time steps using temporal fusion transformer DOI
Louiza Ait Mouloud, Aissa Kheldoun, Deboucha Abdelhakim

и другие.

Journal of Renewable and Sustainable Energy, Год журнала: 2023, Номер 15(5)

Опубликована: Сен. 1, 2023

Accurate prediction of solar irradiance is essential for the successful integration power plants into electrical systems. Despite recent advancements in deep learning technology yielding impressive results forecasting, their lack interpretability has hindered widespread adoption. In this paper, we propose a novel approach that integrates Temporal Fusion Transformer (TFT) with McClear model to achieve accurate and interpretable forecasting performance. The TFT provides transparency its predictions through use self-attention layers long-term dependencies, recurrent local processing, specialized components feature selection, gating suppress extraneous components. capable temporal associations between continuous time-series variables, namely, historical global horizontal (GHI) clear sky GHI, accounting cloud cover variability conditions are often ignored by most machine forecasters. Additionally, it minimizes quantile loss during training produce probabilistic forecasts. study, evaluate performance hourly GHI forecasts on eight diverse datasets varying climates: temperate, cold, arid, equatorial, multiple horizons 2, 3, 6, 12, 24 h. benchmarked against both climatological persistence deterministic Complete History Persistence Ensemble forecasting. To prove our not location locked, been blind tested four completely different datasets. demonstrate proposed outperforms counterparts across all forecast horizons.

Язык: Английский

Процитировано

2

Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications DOI Creative Commons
Stefan Meisenbacher, Johannes Galenzowski, Kevin Förderer

и другие.

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 277 - 297

Опубликована: Окт. 18, 2024

Язык: Английский

Процитировано

0

Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study DOI
Daniel Bayer, Felix Haag, Marco Pruckner

и другие.

2022 7th International Conference on Smart and Sustainable Technologies (SpliTech), Год журнала: 2024, Номер unknown, С. 1 - 6

Опубликована: Июнь 25, 2024

Язык: Английский

Процитировано

0

Short‐Term Load Probability Prediction Based on Integrated Feature Selection and GA‐LSTM Quantile Regression DOI Creative Commons
X. M. Meng, Xigao Shao, Shan Li

и другие.

International Journal of Energy Research, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Accurately forecasting electricity demand is crucial for maintaining the balance between supply and of electric energy in real‐time, ensuring reliability cost‐efficiency power system operations. The integration numerous active loads distributed renewable sources into grid has led to increased load variability, rendering traditional point approach inadequate meeting evolving needs system. Probabilistic forecasting, which predicts complete probability distribution provides more extensive information on uncertainty, emerged as a key solution address these challenges. long short‐term memory (LSTM) model, known its strong performance modeling series, commonly utilized forecasting. Therefore, this study focuses users specific park Yantai. We propose model based integrated feature selection (IFS), genetic algorithm (GA) optimization LSTM, quantile regression (QR), referred IFS‐GA‐QRLSTM model. Initially, method employed identify most influential factors affecting load, optimizing model’s input features reducing data redundancy. To subjective nature parameter LSTM we use GA optimize parameters. combination optimized with QR enables direct generation predictions, are further used kernel density estimation construct distribution. compare proposed five basic models, QRLSTM, IFS‐QRCNN, IFS‐QRRNN, IFS‐QRLSTM, IFS‐QRGRU, prediction, interval prediction. Experimental results demonstrate that paper exhibits better prediction performance, smaller errors, greater effectiveness compared aforementioned models.

Язык: Английский

Процитировано

0

An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model DOI Creative Commons

Andreas Lenk,

Marcus Vogt, Christoph Herrmann

и другие.

Energies, Год журнала: 2024, Номер 18(1), С. 2 - 2

Опубликована: Дек. 24, 2024

The increasing share of renewable energies within energy systems leads to an increase in complexity. growing complexity is due the diversity technologies, ongoing technological innovations, and fluctuating electricity production. To continue ensure a secure, economical, needs-based supply, additional information needed efficiently control these systems. This impacts public industrial supply systems, such as vehicle factories. paper examines influencing factors applicability Temporal Fusion Transformer (TFT) model for weekly demand forecast at automobile production site. Seven different TFT models were trained demand. Six predicted electricity, heat, natural gas. Three used rolling day-ahead forecast, three entire week one step. In seventh model, was again, with target values being same model. analysis shows that forecasting method MAPE 13% already delivers good results predicting electrical prediction accuracy achieved sufficient use outcomes basis operational planning reporting. However, further improvements are still required automated system reduce procurement costs. heat gas demands show too high deviations, 62% 39% accurately predict demands, must be identified explain

Язык: Английский

Процитировано

0