Abstract
The present paper is regarding the reliable forecasting of electrical load demands of the Indian state of Bihar using the long short-term memory (LSTM) technique of machine learning. Bihar is an energy deficit state mainly owing to the lack of resources is now progressing at a good pace which is evident in its growth rate. The prediction of electricity demand, which is crucial for power generation unit planning and monitoring, is hence the need of the hour as this will enable higher growth for the state and prosperity for the people. The dataset of electrical load demand used covering the period of 2019–2020 available on kaggle has been taken from the weekly energy reports of POSOCO, a Government of India enterprise under the Ministry of Power. In the recent times, the transparency of the organizations as well as the development of technologies such as IoT, smart grid and smart energy metres has led to the availability of huge amount of data in the public domain. This involves power consumption as well as generation data of various entities. These developments have led to the need of electrical load forecasting which is useful in the financial planning as well as capacity addition in the generating stations. In this work, we have used deep learning technique called the long short-term memory (LSTM) technique in order to find patterns from a time series data. Results corresponding to the electricity consumptions for years 2019 and 2020 for the state of Bihar are presented and discussed. Finally, the future scope of time series prediction using big data techniques is presented.
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References
Zafer ZD, Hunt LC (2011) Industrial electricity demand for Turkey: a structural time series analysis. Energy Econ 33
Erdogdu E (2007) Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey. Energy Policy 35(2):1129–1146
Pessanha JFM, Leon N (2015) Forecasting long-term electricity demand in the residential sector. Procedia Comput Sci 55:529–538
Keles D, Scelle J, Paraschiv F, Fichtner W (2016) Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl Energy 162:218–230
Sharma V, Tung N, Prakash A (2013) Genetic algorithm optimization for thermal generating unit scheduling and economic dispatch evaluation. Int J Electron Commun Comput Eng 4(5):1575–1580
Hu Z, Ma J, Yang L, Yao L, Pang M (2019) Monthly electricity demand forecasting using empirical mode decomposition-based state space model. Energy Environ 30(7):1236–1254
Lee YW, Tay KG, Choy YY (2018) Forecasting electricity consumption using time series model. Int J Eng Technol 7(4):218
Román-Portabales A, López-Nores M, Pazos-Arias JJ (2021) Systematic review of electricity demand forecast using ANN-based machine learning algorithms. Sensors 21:4544
Example image of a recurrent neural network (rnn). URL http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png.!p.ix, 17
Hochreiter S, Schmidhuber J Long short-term memory. Neural Comput 9(8):1735–1780
Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J (2009) A novel connectionist system for unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell 31(5):855–868
Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471
Gers FA, Schmidhuber E (2001) LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans Neural Networks 12(6):1333–1340
Li X, Wu X (2015) Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4520–4524
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Example image of a long short term memory network (lstm). https://devblogs.nvidia.com/parallelforall/wp-content/uploads/2016/03/LSTM.png
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Prakash, A., Kumar, A., Kaushal, A., Namrata, K., Kumar, N. (2023). Load Forecasting and Analysis of Power Scenario in Bihar Using Time Series Prediction and Machine Learning. In: Namrata, K., Priyadarshi, N., Bansal, R.C., Kumar, J. (eds) Smart Energy and Advancement in Power Technologies. Lecture Notes in Electrical Engineering, vol 926. Springer, Singapore. https://doi.org/10.1007/978-981-19-4971-5_63
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DOI: https://doi.org/10.1007/978-981-19-4971-5_63
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