MDPI - Publisher of Open Access Journals

14 pages, 2557 KB

Open AccessArticle

Enhancing Medium-Term Load Forecasting Accuracy in Post-Pandemic Tropical Regions: A Comparative Analysis of Polynomial Regression, Split Polynomial Regression, and LSTM Networks

by Agus Setiawan

Energies 2025, 18(15), 3999; https://doi.org/10.3390/en18153999 - 27 Jul 2025

Cited by 2 | Viewed by 960

Abstract

This research focuses on medium-term load forecasting in a tropical region post-pandemic. This study presents one of the first attempts to analyze medium-term forecasting using half-hourly resolution in the Java-Bali power system post-COVID-19 period. The dataset comprises load measurements recorded every 30 min (48 data points per day) from 2014 to 2022. Three distinct methods, namely polynomial regression, split polynomial regression, and Long Short-Term Memory (LSTM) networks, were employed and compared to predict the electricity load demand. The analysis found that LSTM outperformed the other methods, exhibiting the lowest error rates with Mean Absolute Percentage Error (MAPE) at 3.86% and Root Mean Squared Error (RMSE) at 1247.93. Additionally, a consistent observation emerged, showing that all methods performed better in predicting load demand during nighttime hours (6 p.m. to 6 a.m.). The hypothesis is that data stability during nighttime, with fewer significant fluctuations, contributed to the improved prediction accuracy. These findings provide valuable insights for improving load forecasting in the post-pandemic tropical region and offer opportunities for enhancing power grid efficiency and reliability. Full article

(This article belongs to the Section F: Electrical Engineering)

► Show Figures

Figure 1

21 pages, 2533 KB

Open AccessArticle

Application of the Holt–Winters Model in the Forecasting of Passenger Traffic at Szczecin–Goleniów Airport (Poland)

by Natalia Drop and Adriana Bohdan

Sustainability 2025, 17(14), 6407; https://doi.org/10.3390/su17146407 - 13 Jul 2025

Cited by 2 | Viewed by 3160

Abstract

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

(This article belongs to the Special Issue Transportation Systems and Infrastructures Planning, Optimization, and Management)

► Show Figures

Figure 1

Search Results (2)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (2)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI