Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting

Published: June 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.

Language: Английский

---

Explainable forecasting of global horizontal irradiance over multiple time steps using temporal fusion transformer

Journal of Renewable and Sustainable Energy, Journal Year: 2023, Volume and Issue: 15(5)

Published: Sept. 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.

Language: Английский

---

Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study

2022 7th International Conference on Smart and Sustainable Technologies (SpliTech), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: June 25, 2024

Language: Английский

---

Short‐Term Load Probability Prediction Based on Integrated Feature Selection and GA‐LSTM Quantile Regression

International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 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.

Language: Английский

---

Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 277 - 297

Published: Oct. 18, 2024

Language: Английский

---

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

Energies, Journal Year: 2024, Volume and Issue: 18(1), P. 2 - 2

Published: Dec. 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

Language: Английский

---