Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting
Electricity load forecasting is crucial for effective energy management, particularly in minimizing energy production and distribution costs. Traditional models like SARIMA and Singular Spectrum Analysis (SSA) have been widely used but often need to capture complex nonlinear patterns and deal with d...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10829849/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832591157208547328 |
|---|---|
| author | Winita Sulandari Yudho Yudhanto Etik Zukhronah Isnandar Slamet Hilman Ferdinandus Pardede Paulo Canas Rodrigues Muhammad Hisyam Lee |
| author_facet | Winita Sulandari Yudho Yudhanto Etik Zukhronah Isnandar Slamet Hilman Ferdinandus Pardede Paulo Canas Rodrigues Muhammad Hisyam Lee |
| author_sort | Winita Sulandari |
| collection | DOAJ |
| description | Electricity load forecasting is crucial for effective energy management, particularly in minimizing energy production and distribution costs. Traditional models like SARIMA and Singular Spectrum Analysis (SSA) have been widely used but often need to capture complex nonlinear patterns and deal with data uncertainties. This study aims to develop and evaluate a hybrid forecasting model that combines the Prophet model with a Neural Network Autoregressive (Prophet-NAR) model, referred to as PropNAR. The objective is to enhance the accuracy of hourly electricity load forecasting in Malaysia by addressing the limitations of existing models. The proposed PropNAR integrated the strengths of the Prophet model in capturing deterministic structures, such as trends, seasonality, and holiday effects with the NAR model’s ability to handle nonlinear stochastic relationships. Additionally, SSA-based bagging is employed to manage data uncertainties, and ensemble techniques are applied to further refine the forecasting accuracy. The hybrid PropNAR model demonstrated significant improvements in forecasting accuracy. Specifically, it reduced Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by approximately 21%-86% compared to the standalone Prophet model. On the Malaysian electricity load datasets, the PropNAR model achieved MAE values ranging from 527.26 to 1023.78, RMSE values from 752.70 to 1498.54, and MAPE values from 1.12% to 2.04%. These results indicate a substantial enhancement over SARIMA and SSA-NAR in handling outliers and data variability. The proposed hybrid PropNAR model offers a robust solution for Malaysian short-term electricity load forecasting, outperforming conventional models in accuracy and reliability. |
| format | Article |
| id | doaj-art-3a9d6075066642e19215caf0f730036c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3a9d6075066642e19215caf0f730036c2025-01-23T00:00:29ZengIEEEIEEE Access2169-35362025-01-01137637764910.1109/ACCESS.2025.352673510829849Hybrid Prophet-NAR Model for Short-Term Electricity Load ForecastingWinita Sulandari0https://orcid.org/0000-0002-8185-1274Yudho Yudhanto1Etik Zukhronah2https://orcid.org/0000-0001-6387-4483Isnandar Slamet3https://orcid.org/0000-0002-6333-7001Hilman Ferdinandus Pardede4https://orcid.org/0000-0001-8078-7592Paulo Canas Rodrigues5https://orcid.org/0000-0002-1248-9910Muhammad Hisyam Lee6https://orcid.org/0000-0002-3700-2363Department of Statistics, Universitas Sebelas Maret, Surakarta, IndonesiaDepartment of Informatics Engineering, Vocational School, Universitas Sebelas Maret, Surakarta, IndonesiaDepartment of Statistics, Universitas Sebelas Maret, Surakarta, IndonesiaDepartment of Statistics, Universitas Sebelas Maret, Surakarta, IndonesiaResearch Center for Artificial Intelligence and Cyber Security, National Research and Innovation Agency, Bandung, IndonesiaDepartment of Statistics, Federal University of Bahia, Salvador, BrazilDepartment of Mathematical Sciences, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaElectricity load forecasting is crucial for effective energy management, particularly in minimizing energy production and distribution costs. Traditional models like SARIMA and Singular Spectrum Analysis (SSA) have been widely used but often need to capture complex nonlinear patterns and deal with data uncertainties. This study aims to develop and evaluate a hybrid forecasting model that combines the Prophet model with a Neural Network Autoregressive (Prophet-NAR) model, referred to as PropNAR. The objective is to enhance the accuracy of hourly electricity load forecasting in Malaysia by addressing the limitations of existing models. The proposed PropNAR integrated the strengths of the Prophet model in capturing deterministic structures, such as trends, seasonality, and holiday effects with the NAR model’s ability to handle nonlinear stochastic relationships. Additionally, SSA-based bagging is employed to manage data uncertainties, and ensemble techniques are applied to further refine the forecasting accuracy. The hybrid PropNAR model demonstrated significant improvements in forecasting accuracy. Specifically, it reduced Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by approximately 21%-86% compared to the standalone Prophet model. On the Malaysian electricity load datasets, the PropNAR model achieved MAE values ranging from 527.26 to 1023.78, RMSE values from 752.70 to 1498.54, and MAPE values from 1.12% to 2.04%. These results indicate a substantial enhancement over SARIMA and SSA-NAR in handling outliers and data variability. The proposed hybrid PropNAR model offers a robust solution for Malaysian short-term electricity load forecasting, outperforming conventional models in accuracy and reliability.https://ieeexplore.ieee.org/document/10829849/Electricity load forecastinghybrid modelprophet modelneural network autoregressive (NAR)singular spectrum analysis (SSA)ensemble techniques |
| spellingShingle | Winita Sulandari Yudho Yudhanto Etik Zukhronah Isnandar Slamet Hilman Ferdinandus Pardede Paulo Canas Rodrigues Muhammad Hisyam Lee Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting IEEE Access Electricity load forecasting hybrid model prophet model neural network autoregressive (NAR) singular spectrum analysis (SSA) ensemble techniques |
| title | Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting |
| title_full | Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting |
| title_fullStr | Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting |
| title_full_unstemmed | Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting |
| title_short | Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting |
| title_sort | hybrid prophet nar model for short term electricity load forecasting |
| topic | Electricity load forecasting hybrid model prophet model neural network autoregressive (NAR) singular spectrum analysis (SSA) ensemble techniques |
| url | https://ieeexplore.ieee.org/document/10829849/ |
| work_keys_str_mv | AT winitasulandari hybridprophetnarmodelforshorttermelectricityloadforecasting AT yudhoyudhanto hybridprophetnarmodelforshorttermelectricityloadforecasting AT etikzukhronah hybridprophetnarmodelforshorttermelectricityloadforecasting AT isnandarslamet hybridprophetnarmodelforshorttermelectricityloadforecasting AT hilmanferdinanduspardede hybridprophetnarmodelforshorttermelectricityloadforecasting AT paulocanasrodrigues hybridprophetnarmodelforshorttermelectricityloadforecasting AT muhammadhisyamlee hybridprophetnarmodelforshorttermelectricityloadforecasting |