IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v353y2024ipas0306261923014435.html

Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP

Author

Listed:
  • van Zyl, Corne
  • Ye, Xianming
  • Naidoo, Raj

Abstract

This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), in the feature selection process for national demand forecasting. Utilising a multi-headed Convolutional Neural Network (CNN), both XAI methods exhibit capabilities in enhancing forecasting accuracy and model efficiency by identifying and eliminating irrelevant features. Comparative analysis revealed Grad-CAM’s exceptional computational efficiency in high-dimensional applications and SHAP’s superior ability in revealing features that degrade forecast accuracy. However, limitations are found in both methods, with Grad-CAM including features that decrease model stability, and SHAP inaccurately ranking significant features. Future research should focus on refining these XAI methods to overcome these limitations and further probe into other XAI methods’ applicability within the time-series forecasting domain. This study underscores the potential of XAI in improving load forecasting, which can contribute significantly to the development of more interpretative, accurate and efficient forecasting models.

Suggested Citation

  • van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014435
    DOI: 10.1016/j.apenergy.2023.122079
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923014435
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122079?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Bommert, Andrea & Sun, Xudong & Bischl, Bernd & Rahnenführer, Jörg & Lang, Michel, 2020. "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    3. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    4. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    5. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
    6. Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    8. Dai, Yeming & Zhao, Pei, 2020. "A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization," Applied Energy, Elsevier, vol. 279(C).
    9. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    10. Chakraborty, Debaditya & Alam, Arafat & Chaudhuri, Saptarshi & Başağaoğlu, Hakan & Sulbaran, Tulio & Langar, Sandeep, 2021. "Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence," Applied Energy, Elsevier, vol. 291(C).
    11. Ghadimi, Noradin & Akbarimajd, Adel & Shayeghi, Hossein & Abedinia, Oveis, 2018. "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting," Energy, Elsevier, vol. 161(C), pages 130-142.
    12. Kazmi, Hussain & Tao, Zhenmin, 2022. "How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead," Applied Energy, Elsevier, vol. 323(C).
    13. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    14. Białek, Jakub & Bujalski, Wojciech & Wojdan, Konrad & Guzek, Michał & Kurek, Teresa, 2022. "Dataset level explanation of heat demand forecasting ANN with SHAP," Energy, Elsevier, vol. 261(PA).
    15. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    16. Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Neubauer, Alexander & Brandt, Stefan & Kriegel, Martin, 2024. "Relationship between feature importance and building characteristics for heating load predictions," Applied Energy, Elsevier, vol. 359(C).
    2. Ran Wu & Mohammad Zoynul Abedin & Hongjun Zeng & Brian Lucey, 2026. "European Union Allowance price forecasting with Multidimensional Uncertainties: A TCN‐iTransformer Approach for Interval Estimation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 45(1), pages 88-113, January.
    3. Taegyum Kim & Hyeontae Jo & Woohyuk Choi & Bong‐Gyu Jang, 2025. "Bitcoin Price Direction Forecasting and Market Variables," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(10), pages 1579-1600, October.
    4. Dong, Xianzhou & Guo, Weiyong & Zhou, Cheng & Luo, Yongqiang & Tian, Zhiyong & Zhang, Limao & Wu, Xiaoying & Liu, Baobing, 2024. "Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods," Energy, Elsevier, vol. 311(C).
    5. Chen, Yong & Lu, Zhiyuan & Liu, Heng & Wang, Hu & Zheng, Zunqing & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based design of target property-oriented fuels using explainable artificial intelligence," Energy, Elsevier, vol. 300(C).
    6. Eskandari, Hamidreza & Saadatmand, Hassan & Ramzan, Muhammad & Mousapour, Mobina, 2024. "Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning," Applied Energy, Elsevier, vol. 366(C).
    7. Wen-Dar Guo & Wei-Bo Chen & Chih-Hsin Chang, 2025. "A spatiotemporal watershed-scale machine-learning model for hourly and daily flood-water level prediction: the case of the tidal Beigang River, Taiwan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(8), pages 9563-9611, May.
    8. Mohammad Ali Sahraei & Keren Li & Qingyao Qiao, 2025. "A Multi-Stage Feature Selection and Explainable Machine Learning Framework for Forecasting Transportation CO 2 Emissions," Energies, MDPI, vol. 18(15), pages 1-26, August.
    9. Wu, Binrong & Lin, Jiacheng & Liu, Rui & Wang, Lin, 2026. "A multi-dimensional interpretable wind speed forecasting model with two-stage feature exploring," Renewable Energy, Elsevier, vol. 256(PB).
    10. de Azevedo Takara, Lucas & Teixeira, Ana Clara & Yazdanpanah, Hamed & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2024. "Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning," Applied Energy, Elsevier, vol. 369(C).
    11. Ji Eun Choi & Ji Won Shin & Dong Wan Shin, 2025. "Vector SHAP Values for Machine Learning Time Series Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 635-645, March.
    12. Shadi, Mohammad Reza & Mirshekali, Hamid & Shaker, Hamid Reza, 2025. "Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).
    13. Kaiyao Jiang & Yuji Yamada, 2025. "A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques," Energies, MDPI, vol. 18(11), pages 1-28, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Runyao Yu & Derek W. Bunn & Julia Lin & Jochen Stiasny & Fabian Leimgruber & Tara Esterl & Yuchen Tao & Lianlian Qi & Yujie Chen & Wentao Wang & Jochen L. Cremer, 2026. "Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets," Papers 2602.10071, arXiv.org, revised May 2026.
    2. Bendiksen, Vidar & Løining, Lars Olai Fjellestad & Lyócsa, Štefan, 2025. "Cross-border and cross-regional electricity transmission: Is there a price impact in south Norway?," Energy Economics, Elsevier, vol. 150(C).
    3. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    4. Adela Bâra & Simona-Vasilica Oprea & Bogdan George Tudorică, 2024. "From the East-European Regional Day-Ahead Markets to a Global Electricity Market," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2525-2557, June.
    5. Aliyon, Kasra & Ritvanen, Jouni, 2024. "Deep learning-based electricity price forecasting: Findings on price predictability and European electricity markets," Energy, Elsevier, vol. 308(C).
    6. Deniz Kenan Kılıç & Peter Nielsen & Amila Thibbotuwawa, 2024. "Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region," Energies, MDPI, vol. 17(12), pages 1-15, June.
    7. Özcan, Abdullah Veli & Erel-Özçevik, Müge & Karaman, Bilal & Baştürk, İlhan & Zeydan, Engin & Taşkın, Sezai & Çetinkaya, Ümit, 2025. "Forecasting day-ahead electricity prices for the electricity market with dynamic time period," Energy, Elsevier, vol. 338(C).
    8. Daniel Manfre Jaimes & Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2023. "A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes," Forecasting, MDPI, vol. 5(3), pages 1-23, July.
    9. Li, Jin & Wen, Xin & Jia, Li & Cao, Ruochen & Zhang, Xiao & Hao, Yanrong & Gao, Chengxin & Cao, Rui, 2025. "A Transformer-based model fusing temporal dependence and variable correlation for short and medium-term electricity price forecasting," Energy, Elsevier, vol. 338(C).
    10. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    11. Tovar Rosas, Mario A. & Pérez, Miguel Robles & Martínez Pérez, E. Rafael, 2022. "Itineraries for charging and discharging a BESS using energy predictions based on a CNN-LSTM neural network model in BCS, Mexico," Renewable Energy, Elsevier, vol. 188(C), pages 1141-1165.
    12. Mascarenhas, Maria Margarida & De Blauwe, Jilles & Amelin, Mikael & Kazmi, Hussain, 2026. "Leveraging asynchronous cross-border market data for improved day-ahead electricity price forecasting in European markets," Applied Energy, Elsevier, vol. 404(C).
    13. Brusaferri, Alessandro & Ballarino, Andrea & Grossi, Luigi & Laurini, Fabrizio, 2025. "On-line conformalized neural networks ensembles for probabilistic forecasting of day-ahead electricity prices," Applied Energy, Elsevier, vol. 398(C).
    14. Xu, Gaoyuan & Shi, Jian & Wu, Jiaman & Lu, Chenbei & Wu, Chenye & Wang, Dan & Han, Zhu, 2024. "An optimal solutions-guided deep reinforcement learning approach for online energy storage control," Applied Energy, Elsevier, vol. 361(C).
    15. Mao, Xuehui & Chen, Shanlin & Yu, Hanxin & Duan, Liwu & He, Yingjie & Chu, Yinghao, 2025. "Simplicity in dynamic and competitive electricity markets: A case study on enhanced linear models versus complex deep-learning models for day-ahead electricity price forecasting," Applied Energy, Elsevier, vol. 383(C).
    16. Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025. "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, vol. 141(C).
    17. Mohammed Jasim M. Al Essa, 2025. "A review on price-driven energy management systems and demand response programs in smart grids," Environment Systems and Decisions, Springer, vol. 45(1), pages 1-22, March.
    18. Liu, Tianhao & Li, Fangning & Zhang, Dongdong & Shan, Linke & Zhu, Hongyu & Du, Pengcheng & Jiang, Meihui & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Kong, Fannie, 2026. "Intelligent load forecasting technologies for diverse scenarios in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
    19. Pablo Alejandro Mendez-Santos & Nathalia Alexandra Chacón-Reino & Luis Fernando Guerrero-Vásquez & Jorge Osmani Ordoñez-Ordoñez & Paul Andrés Chasi-Pesantez, 2025. "Estimation and Forecasting of the Average Unit Cost of Energy Supply in a Distribution System Using Multiple Linear Regression and ARIMAX Modeling in Ecuador," Energies, MDPI, vol. 18(14), pages 1-33, July.
    20. Shadi Tehrani & Jesús Juan & Eduardo Caro, 2022. "Electricity Spot Price Modeling and Forecasting in European Markets," Energies, MDPI, vol. 15(16), pages 1-23, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014435. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.