Comparative Analysis of Different Machine Learning Techniques Along with Hyper-Parameter Optimization for Prediction of Small Data Set Long Term Electricity Demand of Assam
Keywords:
Energy, Forecasting, Machine learning, Performance, Small datasetAbstract
Electricity is a prime and compulsory source for development of any nation. Electricity demand of Assam is increasing at an alarming rate and to compensate this loss energy is generated from many non-renewable resources, as a result Assam fails to predict and generate the future electricity demand sustainably. This is due to many factors like rapidly rising population, literacy rate, industrialization, GDP and standard of living. Therefore, predicting the future electricity demand accurately will help in better decision making in implementing energy policies and provide information regarding future energy requirements and how to generate it sustainably. In this paper an attempt has been made to assess the effectiveness of different machine learning (ML) techniques for forecasting long term electricity demand of Assam for small dataset. The data collected is a small dataset consisting of several attributes that influence the energy consumption of Assam. A small dataset has been chosen as in many cases it is challenging to see that any product or system that has been in the market for a small time need to be predicted accurately for its future demand. So, the performance of the predicted results of the ML techniques will help in understanding the challenges of predicting small dataset. And to better understand the relationship between the attributes and the response Partial Dependence Plot has been used.
Downloads
References
Anand A., & Suganthi L. (2018). Hybrid GA-PSO optimization of artificial neural network for forecasting electricity demand. Energies, 11(4), 728.
Assam energy vision draft of APDCL (2018).
Aylin C¸ig˘dem Ko¨ne, Tayfun Bu¨ ke (2010) Forecasting of CO2 emissions from fuel combustion using trend analysis. Elsevier, Renewable and sustainable energy reviews 14 2906-2915
Balachandran S, Velavan R and Rudramoorthy R. (2009) Energy Demand Forecasting for a District in Tamilnadu, India - An Analytical Approach. RERIC, International Energy Journal 10 187-194
Baruah D. C., & Bora G. C. (2008). Energy demand forecast for mechanized agriculture in rural India. Energy Policy, 36(7), 2628-2636.
Chaturvedi S., Rajasekar E., Natarajan S., & McCullen N. (2022). A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. Energy Policy, 168, 113097.
Debnath Kumar Biswajit, Mourshed Monjur (2018) Forecasting methods in energy planning models. Elsevier, Renewable and sustainable energy reviews 88, 297-325
Kale R. V., & Pohekar S. D. (2014). Electricity demand and supply scenarios for Maharashtra (India) for 2030: An application of long range energy alternatives planning. Energy Policy, 72, 1-13.
Kumar A., Mittal A., & Pradhan A. K. (2022). Magnitude and determinants of energy intensity: evidence from Indian firms. Environmental Science and Pollution Research, 1-12.
L. Suganthi, Anand A. Samuel (2012) Energy models for demand forecasting—A review. Elsevier, Renewable and sustainable energy reviews 16, 1223-1240
Madeline Hui Li Lee, Yee Chee Ser et. al. (2022) A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models. Mathematics 2022, 10, 1329. https://doi.org/10.3390/math10081329
Mubarok Hussain, Septyawan Ryan (2018) Analysis of PLN’s Electrical Energy Demand in the Area of Batam-Indonesia Using the Linear Regression Method. 2018 4th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia
Nandi Sumit and Basak Pijush (2014) Carbon Dioxide Emission in Assam, India- A Mathematical Study Based on Attributes. Res J. Chem. Environ. Sci. Vol 2 [4] August: 39-46
N Sumit and B. Pijush (2014) Carbon Dioxide Emission in Assam, India- A Mathematical Study Based on Attributes. Res. J. Chem. Env. Sci. Vol 2 [4] August. 39-46
Pokharel S. (2007) An econometric analysis of energy consumption in Nepal. Energy Policy 35 350–361
Pradhan A. K., Rout S., & Khan I. A. (2021). Does market concentration affect wholesale electricity prices? An analysis of the Indian electricity sector in the COVID-19 pandemic context. Utilities Policy, 73, 101305.
Qiang Wang, Shuyu Li, Rongrong Li (2018) Forecasting Energy Demand in China and India: Using Single-linear, Hybridlinear, and Non-linear Time Series Forecast Technique. Energy (2018), doi: 10.1016/j.energy.2018.07.168
Sachan A., Sahu U. K., Pradhan A. K., & Thomas R. (2023). Examining the drivers of renewable energy consumption: Evidence from BRICS nations. Renewable Energy, 202, 1402-1411.
Sang-Bing Tsai, Youzhi Xue et al. (2016) Models for forecasting growth trends in renewable energy. Elsevier, Renewable and sustainable energy reviews
Saravanan S., Kannan S. and Thangaraj C. (2012) India’s electricity demand forecast using regression analysis and artificial neural networks based on principal components. ICTACT journal on soft computing, july, volume: 02, issue: 04
Statistical handbook of Assam (2018)
Trend analysis of GHG emissions in Assam (2013). GHG platform India
Ullash K. Rout, Alfred Vo, Singh Anoop et al. (2011) Energy and emissions forecast of China over a long-time horizon. Elsevier, Energy 36 1-11
Wang Q., Li S., & Li R. (2018). Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques. Energy, 161, 821-831.
Wen-jing Niu, Zhong-kai Feng et. al. (2021) Short-term electricity load time series prediction by machine learning model via feature selection and parameter optimization using hybrid cooperation search algorithm. IOP Publishing; Environmental Research Letters 16 055032
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
