No Access

Emotion-Driven Energy Load Forecasting: An Ensemble Leveraging Insights from News

Charalampos M. Liapis

https://orcid.org/0000-0002-4717-031X

Department of Mathematics, University of Patras, Rion, 26504 Patras, Achaea, Greece

Computer Technology Institute and Press “Diophantus”, Rion, 26504 Patras, Achaea, Greece

E-mail Address: c.liapis@ac.upatras.gr

Search for more papers by this author

Aikaterini Karanikola

https://orcid.org/0009-0006-4226-6597

Department of Mathematics, University of Patras, Rion, 26504 Patras, Achaea, Greece

E-mail Address: karanikola@upatras.gr

Search for more papers by this author

, and

Sotiris Kotsiantis

https://orcid.org/0000-0002-2247-3082

Department of Mathematics, University of Patras, Rion, 26504 Patras, Achaea, Greece

E-mail Address: sotos@math.upatras.gr

Corresponding author.

Search for more papers by this author

https://doi.org/10.1142/S0218213024500131Cited by:0 (Source: Crossref)

Abstract

In modern times, system energy load forecasting is an extremely important process in a variety of contexts. Moreover, energy load time series fluctuations are influenced by a wide range of factors, ranging, inter alia, from environmental conditions, natural events, and demographics to both regional and global geopolitical contexts, economic conditions, energy sources, policies, and regulations. Given these, this paper examines the integration of news information from the global scene into Greek energy load forecasting schemes through the use of sentiment analysis. Investigating the ways the general emotional footprint of news worldwide affects and can be used in an energy modeling context, we benchmark possible ensemble configurations incorporating a multitude of 31 emotion polarities. Building on our previous work, an ensemble method that exploits specific outputs from a multi-label sentiment classifier and a sentiment analysis procedure under a multivariate forecasting scheme is presented. It is shown, through an empirical evaluation of the results, that the integration of emotion representations related to non-Greek news concerning global current affairs improves the predictions.

Keywords:

Remember to check out the Most Cited Articles!
Check out Notable Titles in Artificial Intelligence.

References

1. C. M. Liapis, A. Karanikola and S. Kotsiantis , Energy load forecasting: Investigating mid-term predictions with ensemble learners, in Artificial Intelligence Applications and Innovations, eds. I. Maglogiannis, L. Iliadis, J. Macintyre and P. Cortez (Springer International Publishing, Cham, 2022), pp. 343–355. Crossref, Google Scholar
2. C. M. Liapis and S. Kotsiantis , Energy balance forecasting: An extensive multivariate regression models comparison, in Proc. of the 12th Hellenic Conf. on Artificial Intelligence (SETN ’22) (Association for Computing Machinery, New York, NY, 2022). Crossref, Google Scholar
3. C. M. Liapis, A. Karanikola and S. B. Kotsiantis , A multivariate ensemble learning method for medium-term energy forecasting, Neural Computing and Applications 35 (2023) 21479–21497. Crossref, Web of Science, Google Scholar
4. D. Alberg and M. Last , Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms, Vietnam Journal of Computer Science 5 (2018) 241–249. Crossref, Google Scholar
5. Y. Wang, W. Zhang, X. Li, A. Wang, T. Wu and Y. Bao , Monthly load forecasting based on an ARIMA-BP model: A case study on Shaoxing city, in 12th IEEE PES Asia-Pacific Power and Energy Engineering Conf. (APPEEC), Nanjing, China, 14-16 September 2020, Vol. 1 (2020), pp. 1–6. Crossref, Google Scholar
6. E. M. de Oliveira and F. L. Cyrino Oliveira , Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods, Energy 144 (2018) 776–788. Crossref, Web of Science, Google Scholar
7. M. A. Hammad, B. Jereb, B. Rosi and D. Dragan , Methods and models for electric load forecasting: A comprehensive review, Methods 11(1) (2020) 51–76. Google Scholar
8. S. N. Fallah, R. C. Deo, M. Shojafar, M. Conti and S. Shamshirband , Computational intelligence approaches for energy load forecasting in smart energy management grids: State of the art, future challenges, and research directions, Energies 11(3) (2018) 596. Crossref, Web of Science, Google Scholar
9. T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang and H. Zareipour , Energy forecasting: A review and outlook, IEEE Open Access Journal of Power and Energy 7 (2020) 376–388. Crossref, Google Scholar
10. J. Luo, T. Hong, Z. Gao and S.-C. Fang , A robust support vector regression model for electric load forecasting, International Journal of Forecasting 39(2) (2023) 1005–1020. Crossref, Web of Science, Google Scholar
11. P. Vrablecová, A. Bou Ezzeddine, V. Rozinajová, S. Šárik and A. K. Sangaiah , Smart grid load forecasting using online support vector regression, Computers & Electrical Engineering 65 (2018) 102–117. Crossref, Web of Science, Google Scholar
12. Y. Dong, Z. Zhang and W.-C. Hong , A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting, Energies 11(4) (2018) 1009. Crossref, Web of Science, Google Scholar
13. J. Moon, Y. Kim, M. Son and E. Hwang , Hybrid short-term load forecasting scheme using random forest and multilayer perceptron, Energies 11(12) (2018) 3283. Crossref, Web of Science, Google Scholar
14. Y. Lu, Y. Li, D. Xie, E. Wei, X. Bao, H. Chen and X. Zhong , The application of improved random forest algorithm on the prediction of electric vehicle charging load, Energies 11(11) (2018) 3207. Crossref, Web of Science, Google Scholar
15. Z. Wang, Y. Wang, R. Zeng, R. S. Srinivasan and S. Ahrentzen , Random forest based hourly building energy prediction, Energy and Buildings 171 (2018) 11–25. Crossref, Web of Science, Google Scholar
16. G.-F. Fan, M. Yu, S.-Q. Dong, Y.-H. Yeh and W.-C. Hong , Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling, Utilities Policy 73 (2021) 101294. Crossref, Web of Science, Google Scholar
17. P.-H. Kuo and C.-J. Huang , A high precision artificial neural networks model for short-term energy load forecasting, Energies 11(1) (2018) 213. Crossref, Web of Science, Google Scholar
18. G. D. Merkel, R. J. Povinelli and R. H. Brown , Short-term load forecasting of natural gas with deep neural network regression, Energies 11(8) (2018) 2008. Crossref, Web of Science, Google Scholar
19. B.-S. Kwon, R.-J. Park and K.-B. Song , Short-term load forecasting based on deep neural networks using LSTM layer, Journal of Electrical Engineering & Technology 15 (2020) 1501–1509. Crossref, Web of Science, Google Scholar
20. S. H. Rafi, Nahid-Al-Masood, S. R. Deeba and E. Hossain , A short-term load forecasting method using integrated CNN and LSTM network, IEEE Access 9 (2021) 32436–32448. Crossref, Web of Science, Google Scholar
21. X. Tang, Y. Dai, T. Wang and Y. Chen , Short-term power load forecasting based on multi-layer bidirectional recurrent neural network, IET Generation, Transmission & Distribution 13(17) (2019) 3847–3854. Crossref, Web of Science, Google Scholar
22. T. Hossen, A. S. Nair, R. A. Chinnathambi and P. Ranganathan , Residential load forecasting using deep neural networks (DNN), in 2018 North American Power Symposium (NAPS), Fargo, ND, USA, 9-11 September 2018, pp. 1–5. Google Scholar
23. S. Kumar, L. Hussain, S. Banarjee and M. Reza , Energy load forecasting using deep learning approach-LSTM and GRU in spark cluster, in 2018 Fifth Int. Conf. on Emerging Applications of Information Technology (EAIT), Kolkata, India, 12-13 January 2018, pp. 1–4. Google Scholar
24. X. Guo, Q. Zhao, D. Zheng, Y. Ning and Y. Gao , A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price, Energy Reports 6 (2020) 1046–1053. Crossref, Web of Science, Google Scholar
25. Y. Wang, N. Zhang and X. Chen , A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features, Energies 14(10) (2021) 2737. Crossref, Web of Science, Google Scholar
26. L. Xu, S. Wang and R. Tang , Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load, Applied Energy 237 (2019) 180–195. Crossref, Web of Science, Google Scholar
27. C. M. Liapis, A. Karanikola and S. Kotsiantis , Investigating deep stock market forecasting with sentiment analysis, Entropy 25(2) (2023) 219. Crossref, Web of Science, Google Scholar
28. C. M. Liapis, A. Karanikola and S. Kotsiantis , A multi-method survey on the use of sentiment analysis in multivariate financial time series forecasting, Entropy 23(12) (2021) 1603. Crossref, Web of Science, Google Scholar
29. Z. Jin, Y. Yang and Y. Liu , Stock closing price prediction based on sentiment analysis and LSTM, Neural Computing and Applications 32 (2020) 9713–9729. Crossref, Web of Science, Google Scholar
30. X. Li, P. Wu and W. Wang , Incorporating stock prices and news sentiments for stock market prediction: A case of hong kong, Information Processing & Management 57(5) (2020) 102212. Crossref, Web of Science, Google Scholar
31. J. Abraham, D. Higdon, J. Nelson and J. Ibarra , Cryptocurrency price prediction using tweet volumes and sentiment analysis, SMU Data Science Review 1(3) (2018) 1–21. Google Scholar
32. S. M. Raju and A. M. Tarif, Real-time prediction of Bitcoin price using machine learning techniques and public sentiment analysis, arXiv preprint arXiv:abs/2006.14473 (2020). Google Scholar
33. A. Zeroual, F. Harrou, A. Dairi and Y. Sun , Deep learning methods for forecasting COVID-19 time-series data: A comparative study, Chaos, Solitons & Fractals 140 (2020) 110121. Crossref, Web of Science, Google Scholar
34. W. Budiharto and M. Meiliana , Prediction and analysis of indonesia presidential election from twitter using sentiment analysis, Journal of Big Data 5 (2018) 51. Crossref, Google Scholar
35. Gov.gr, Government of Greece — Public sector: Energy system load (2022), https://www.data.gov.gr/datasets/admie_realtimescadasystemload/. Google Scholar
36. Gov.gr, Government of Greece — Public sector: Energy balance (2022), https://www.data.gov.gr/datasets/admie_dailyenergybalanceanalysis/. Google Scholar
37. R. Misra, News category dataset, arXiv preprint arXiv:2209.11429 (2022). Google Scholar
38. R. Misra and J. Grover , Sculpting Data for ML: The first act of Machine Learning (Jigyasa Grover & Rishabh Misra, 2021). Google Scholar
39. P. J. Huber , Robust estimation of a location parameter, The Annals of Mathematical Statistics 35(1) (1964) 73–101. Crossref, Google Scholar
40. F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw and W. A. Stahel , Robust Statistics: The Approach Based on Influence Functions (Wiley-Interscience, 1986). Google Scholar
41. Y. Feng and Q. Wu , A statistical learning assessment of Huber regression, Journal of Approximation Theory 273 (2022) 105660. Crossref, Web of Science, Google Scholar
42. S. Mallat and Z. Zhang , Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing 41 (1993) 3397–3415. Crossref, Web of Science, Google Scholar
43. R. Rubinstein, M. Zibulevsky and M. Elad, Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit, (CS Technion: Haifa, Israel, 2008); Available online: http://www.cs.technion.ac.il/ $\sim$ $\sim$ ronrubin/Publications/KSVD-OMP-v2.pdf (accessed on 25 July 2023). Google Scholar
44. S. Loria , Textblob Documentation, Release 0.15, Vol. 2 (2018), https://textblob.readthedocs.io/en/dev/. Google Scholar
45. C. Hutto and E. Gilbert , VADER: A parsimonious rule-based model for sentiment analysis of social media text, proceedings of the International AAAI Conference on Web and Social Media 8(1) (2014) 216–225, https://doi.org/10.1609/icwsm.v8i1.14550. Crossref, Google Scholar
46. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:abs/1810.04805 (2019). Google Scholar
47. D. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade and S. Ravi , Goemotions: A dataset of fine-grained emotions, in Proc. of the 58th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics, 2020), pp. 4040–4054. Crossref, Google Scholar
48. F. N. Iandola, A. E. Shaw, R. Krishna and K. W. Keutzer, SqueezeBERT: What can computer vision teach NLP about efficient neural networks?, arXiv preprint arXiv:2006.11316 (2020). Google Scholar
49. M. Friedman , The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association 32 (1937) 675–701. Crossref, Google Scholar
50. O. J. Dunn , Multiple comparisons among means, Journal of the American Statistical Association 56 (1961) 52–64. Crossref, Web of Science, Google Scholar
51. E. S. Yilmaz, A. Ozpolat and M. A. Destek , Do twitter sentiments really effective on energy stocks? Evidence from the intercompany dependency, Environmental Science and Pollution Research 29 (2022) 78757–78767. Crossref, Google Scholar
52. R. Cai, B. Qin, Y. Chen, L. Zhang, R. Yang, S. Chen and W. Wang , Sentiment analysis about investors and consumers in energy market based on BERT-BiLSTM, IEEE Access 8 (2020) 171408–171415. Crossref, Web of Science, Google Scholar
53. J. C. Reboredo and A. Ugolini , The impact of Twitter sentiment on renewable energy stocks, Energy Economics 76 (2018) 153–169. Crossref, Web of Science, Google Scholar
54. D. P. Panagoulias, E. Sarmas, V. Marinakis, M. Virvou, G. A. Tsihrintzis and H. Doukas , Intelligent decision support for energy management: A methodology for tailored explainability of artificial intelligence analytics, Electronics 12(21) (2023) 4430. Crossref, Web of Science, Google Scholar