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Short-Term Load Demand Forecasting Based on Weather and Influencing Factors Using Deep Neural Network Experts for Sustainable Development Goal 7

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Abstract

Due to the increasing essentiality of electricity in this modern world, its sustainable management is crucial for the economic development of a nation, and is allied with the Sustainable Development Goal 7, which focuses on ensuring access to affordable and reliable energy for all. Accurate day-ahead instantaneous load forecasting, also known as Short-Term Load Forecasting, plays a pivotal role in achieving higher efficiency and sustainability in power system planning and operation. Due to the volatility and randomness of electricity demand as well as high dependence on exogenous parameters such as weather, effectively capturing the dynamic change of load curves is extremely challenging. Including knowledge of influencing parameters can significantly enhance the forecasting accuracy, reduce service interruptions, and ultimately lead to cost reduction and reliability. Based on a systematic literature review, it is characterized that an optimal forecasting system learns patterns from historic load consumption during similar days along with consideration of various influencing parameters. Hence, we propose a novel load forecasting system MoDeNNFE that employs a Mixture of Experts to produce load forecast output. To optimize the forecasting accuracy, the system extracts different input features from historical data based on types of day, and each expert member undergoes Deep Learning to make specialized predictions accordingly. We perform experimental research for Indian metropolitan cities using data of its prominent power utility Tata Power and India Meteorological Department. The results and comparisons indicate that MoDeNNFE achieves superior predictive performance over other standard approaches and demonstrates a promising forecasting efficacy.

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References

  1. Su P, Tian X, Wang Y, Deng S, Zhao J, An Q, Wang Y. Recent trends in load forecasting technology for the operation optimization of distributed energy system. Energies. 2017. https://doi.org/10.3390/en10091303.

    Article  Google Scholar 

  2. Hong T, Shahidehpour M. “Load Forecasting Case Study,” Eastern Interconnection States’ Planning Council, 2015.

  3. Sinha A, Tayal R, Vyas A, Pandey P, Vyas OP. Forecasting electricity load with hybrid scalable model based on stacked non linear residual approach. Front Energy Res. 2021. https://doi.org/10.3389/fenrg.2021.720406.

    Article  Google Scholar 

  4. Feinberg E, Genethliou D. Load forecasting. In: Chow J, Wu F, Momoh J, editors. Applied mathematics for restructured electric power systems. Springer; 2005. https://doi.org/10.1007/0-387-23471-3_12.

    Chapter  Google Scholar 

  5. Klyuev R, Morgoev I, Morgoeva A, Gavrina O, Martyushev N, Efremenkov E, Mengxu Q. Methods of forecasting electric energy consumption: a literature review. Energies. 2022. https://doi.org/10.3390/en15238919.

    Article  Google Scholar 

  6. Staffell I, Pfenninger S. The increasing impact of weather on electricity supply and demand. Energy. 2018. https://doi.org/10.1016/j.energy.2017.12.051.

    Article  Google Scholar 

  7. Methaprayoon K, Lee W, Rasmiddatta S, Liao J, Ross R. Multistage artificial neural network short-term load forecasting engine with front-end weather forecast. IEEE Trans Ind Appl. 2007. https://doi.org/10.1109/TIA.2007.908190.

    Article  Google Scholar 

  8. Guan Y, Wang J. Uncertainty sets for robust unit commitment. IEEE Trans Power Syst. 2014. https://doi.org/10.1109/TPWRS.2013.2288017.

    Article  Google Scholar 

  9. Nair S, Hosalikar KS. Trends in surface temperature variability over Mumbai. Mausam. 2013. https://doi.org/10.54302/mausam.v64i2.683.

    Article  Google Scholar 

  10. Maral SG, Mukhopadhyay T. Signal of urban heat island (UHI) effect: a case study of Mumbai metropolitan region. Mausam. 2015. https://doi.org/10.54302/mausam.v66i4.580.

    Article  Google Scholar 

  11. Pai D, Nair S, Ramanathan A. Long term climatology and trends of heat waves over India during the recent 50 years (1961–2010). Mausam. 2013. https://doi.org/10.54302/mausam.v64i4.742.

    Article  Google Scholar 

  12. E. Bureau, “India to soon give decadal climate forecast: M Rajeevan, Secretary, Ministry of Earth Sciences,” The Economic Times, 2020. Accessible: https://economictimes.indiatimes.com/news/economy/agriculture/india-to-soon-give-decadal-climate-forecast-m-rajeevan-secretary-ministry-of-earth-sciences/articleshow/74096669.cms

  13. Chakravarty K, Arun N, Yadav P, Bhangale R, Murugavel P, Kanawade V, Mohmmad J, Hosalikar K, Pandithurai G. Characteristics of precipitation microphysics during Tropical Cyclone Nisarga (2020) as observed over the orographic region of Western Ghats in the Indian sub-continent. Atmos Res. 2021. https://doi.org/10.1016/j.atmosres.2021.105861.

    Article  Google Scholar 

  14. Yadav N, Telange N, Gole M, Pallikuth D. Impact of very severe cyclone ‘Nisarga’ on Mumbai Tata power system operation. Int J Eng Res Technol, vol. 9, 2020.

  15. Gallo Cassarino T, Sharp E, Barrett M. The impact of social and weather drivers on the historical electricity demand in Europe. Appl Energy. 2018. https://doi.org/10.1016/j.apenergy.2018.07.108.

    Article  Google Scholar 

  16. Li G, Liu C, Mattson C, Lawarrée J. Day-ahead electricity price forecasting in a grid environment. IEEE Trans Power Syst. 2007. https://doi.org/10.1109/TPWRS.2006.887893.

    Article  Google Scholar 

  17. Sen S. “Power demand dips by 12–26% in Maharashtra,” Times of India, 2020. Accessible: https://timesofindia.indiatimes.com/city/mumbai/power-demand-dips-by-12-26-in-maharashtra/articleshow/75151033.cms.

  18. Lobo S, Pallikuth D. Impact of COVID-19 related shutdowns on utility-scale electric demand and forecasting: an Indian Metropolitan area case study, White Paper - BluWaveAI, 2020.

  19. NITI Aayog, National Strategy for Artificial Intelligence, 2018.

  20. Hou Y, Mu H, Dong G, Shi J. Influences of urban temperature on the electricity consumption of Shanghai. Adv Climate Change Research. 2014. https://doi.org/10.3724/SP.J.1248.2014.074.

    Article  Google Scholar 

  21. Wood A, Wollenberg B, Sheblé G. Power generation, operation, and control. 3rd ed. Wiley; 2013.

    Google Scholar 

  22. Metcalfe A, Cowpertwait P. Introductory time series with R. Springer; 2009.

    Book  Google Scholar 

  23. George B. Box and jenkins: time series analysis, forecasting and control. In: A very British affair. London: Palgrave; 2013. https://doi.org/10.1057/9781137291264_6.

    Chapter  Google Scholar 

  24. Vanting N, Ma Z, Jørgensen B. A scoping review of deep neural networks for electric load forecasting. Energy Inform. 2021. https://doi.org/10.1186/s42162-021-00148-6.

    Article  Google Scholar 

  25. Fallah S, Deo R, Shojafar M, Conti M, Shamshirband S. Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions. Energies (Basel). 2018. https://doi.org/10.3390/en11030596.

    Article  Google Scholar 

  26. Luo X, Oyedele L, Ajayi A, Akinade O. Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads. Sustain Cities Soc. 2020. https://doi.org/10.1016/j.scs.2020.102283.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ji P, Xiong D, Wang P, Chen J. A Study on Exponential Smoothing Model for Load Forecasting. In: 2012 Asia-Pacific Power and Energy Engineering Conference, IEEE, 2012. https://doi.org/10.1109/APPEEC.2012.6307555.

  28. Kuster C, Rezgui Y, Mourshed M. Electrical load forecasting models: a critical systematic review. Sustain Cities Soc. 2017. https://doi.org/10.1016/j.scs.2017.08.009.

    Article  Google Scholar 

  29. Tanidir O, Tör OB. Accuracy of ANN based day-ahead load forecasting in Turkish power system: degrading and improving factors. Neural Netw World. 2015;25(4):443–56. https://doi.org/10.14311/NNW.2015.25.023.

    Article  Google Scholar 

  30. Silva Ortega J, Cervantes-Bolivar B, Isaac Millan I, Cardenas Escorcia Y, Valencia-Ochoa G. Demand energy forecasting using genetic algorithm to guarantee safety on electrical transportation system. Chem Eng Trans. 2018. https://doi.org/10.3303/CET1867132.

    Article  Google Scholar 

  31. Zhang X, Wang J, Zhang K. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electric Power Syst Res. 2017. https://doi.org/10.1016/j.epsr.2017.01.035.

    Article  Google Scholar 

  32. de Aquino R, Ferreira A, Carvalho M, Lira M, Silva G, Neto O. Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system. In: Lecture Notes in Computer Science, vol. 4132, LNCS-II. Springer, 2006. https://doi.org/10.1007/11840930_79.

  33. Li B, Zhang J, He Y, Wang Y. Short-term load-forecasting method based on wavelet decomposition with second-order gray neural network model combined with ADF test. IEEE Access. 2017. https://doi.org/10.1109/ACCESS.2017.2738029.

    Article  Google Scholar 

  34. Wang Y, Chen Q, Sun M, Kang C, Xia Q. An ensemble forecasting method for the aggregated load with subprofiles. IEEE Trans Smart Grid. 2018. https://doi.org/10.1109/TSG.2018.2807985.

    Article  Google Scholar 

  35. Buitrago J, Asfour S. Short-term forecasting of loads using nonlinear autoregressive neural networks with exogenous vector inputs. Energies. 2017. https://doi.org/10.3390/en10010040.

    Article  Google Scholar 

  36. Mordjaoui M, Boudjema B, Daira R. Short term electric load forecasting using neuro-fuzzy modeling for nonlinear system identification. In: 3rd Conference on nonlinear science and complexity, Ankara, 2010.

  37. Fay D, Ringwood J. On the influence of weather forecast errors in short-term load forecasting models. IEEE Trans Power Syst. 2010. https://doi.org/10.1109/TPWRS.2009.2038704.

    Article  Google Scholar 

  38. Kong W, Dong Z, Hill D, Luo F, Xu Y. Short-term residential load forecasting based on resident behaviour learning. IEEE Trans Power Syst. 2018. https://doi.org/10.1109/TPWRS.2017.2688178.

    Article  Google Scholar 

  39. Bianchi F, Maiorino E, Kampffmeyer M, Rizzi A, Jenssen R. An overview and comparative analysis of recurrent neural networks for short term load forecasting. Neural Evol Comput. 2017. https://doi.org/10.1007/978-3-319-70338-1.

    Article  Google Scholar 

  40. Zahid M, Ahmed F, Javaid N, Abbasi R, Kazmi H, Javaid A, Bilal M, Akbar M, Ilahi M. Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics. 2019. https://doi.org/10.3390/electronics8020122.

    Article  Google Scholar 

  41. Kong W, Dong Z, Jia Y, Hill D, Xu Y, Zhang Y. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid. 2019. https://doi.org/10.1109/TSG.2017.2753802.

    Article  Google Scholar 

  42. Niu W, Feng Z, Li S, Wu H, Wang J. Short-term electricity load time series prediction by machine learning model via feature selection and parameter optimization using hybrid cooperation search algorithm. Environ Res Lett. 2021. https://doi.org/10.1088/1748-9326/abeeb1.

    Article  Google Scholar 

  43. Bouktif S, Fiaz A, Ouni A, Serhani M. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies. 2018. https://doi.org/10.3390/en11071636.

    Article  Google Scholar 

  44. Eapen R, Simon S. Performance analysis of combined similar day and day ahead short term electrical load forecasting using sequential hybrid neural networks. IETE J Res. 2019. https://doi.org/10.1080/03772063.2017.1417749.

    Article  Google Scholar 

  45. Yousaf A, Asif R, Shakir M, Rehman A, Adrees M. An improved residential electricity load forecasting using a machine-learning-based feature selection approach and a proposed integration strategy. Sustainability. 2021. https://doi.org/10.3390/su13116199.

    Article  Google Scholar 

  46. Raza M, Khosravi A. A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev. 2015. https://doi.org/10.1016/j.rser.2015.04.065.

    Article  Google Scholar 

  47. Qi X, Zheng X, Chen Q (2020) A short term load forecasting of integrated energy system based on CNN-LSTM. In: International Conference on Energy, Environment and Bioengineering, Energy Engineering and Power System, vol. 185, E3S Web of Conferences, 2020. https://doi.org/10.1051/e3sconf/202018501032.

  48. Kim T, Cho S. Particle swarm optimization-based CNN-LSTM networks for forecasting energy consumption. In: 2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2019. https://doi.org/10.1109/CEC.2019.8789968.

  49. Pramono S, Rohmatillah M, Maulana E, Hasanah R, Hario F. Deep learning-based short-term load forecasting for supporting demand response program in hybrid energy system. Energies (Basel). 2019. https://doi.org/10.3390/en12173359.

    Article  Google Scholar 

  50. Ai S, Chakravorty A, Rong C. Household energy consumption prediction using evolutionary ensemble neural network. In: Engineering assets and public infrastructures in the age of digitalization. Lecture Notes in Mechanical Engineering. Springer; 2020. https://doi.org/10.1007/978-3-030-48021-9_102.

    Chapter  Google Scholar 

  51. Agrawal K, Garg S, Sharma S, Patel P, Bhatnagar A. Fusion of statistical and machine learning approaches for time series prediction using earth observation data. Int J Comput Sci Eng. 2017. https://doi.org/10.1504/IJCSE.2017.084159.

    Article  Google Scholar 

  52. Bot K, Ruano A, da Graça Ruano M. Forecasting electricity consumption in residential buildings for home energy management systems, Communications in Computer and Information Science. Springer, 2020. https://doi.org/10.1007/978-3-030-50146-4_24.

  53. Chollet F. Keras, 2015, Accessible: https://keras.io.

  54. Vohra S. Cyclone Tauktae exposes vulnerabilities along India’s west coast, Mongabay Series: Flood and Drought, 2021. Accessible: https://india.mongabay.com/2021/05/cyclone-tauktae-exposes-vulnerabilities-along-indias-west-coast/.

  55. IMD, MoES, Press Release – 14th June 2022. Accessible: https://internal.imd.gov.in/press_release/20220614_pr_1672.pdf

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Authors and Affiliations

  1. K. J. Somaiya Institute of Technology, University of Mumbai, Mumbai, India

    Radhika Kotecha, Parth Maniar & Saj Maru

  2. K. J. Somaiya College of Engineering, Somaiya Vidyavihar University, Mumbai, India

    Suresh Ukarande

  3. India Meteorological Department, Ministry of Earth Sciences, Pune, India

    Krishnanand Hosalikar

  4. The Tata Power Company Ltd., Mumbai, India

    Devanand Pallikuth, Trusha Biswas & Vismay Rane

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Kotecha, R., Ukarande, S., Hosalikar, K. et al. Short-Term Load Demand Forecasting Based on Weather and Influencing Factors Using Deep Neural Network Experts for Sustainable Development Goal 7. SN COMPUT. SCI. 5, 253 (2024). https://doi.org/10.1007/s42979-023-02587-2

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