Abstract
Forecasting of load refers to prediction of power demanded by the targeted geographical area based on the trends and patterns of previous load demands. To forecast the load accurately, is one of the biggest challenges of all electrical utilities and load dispatch centres. In this paper, artificial neural network (ANN) and time series (TS) models are used to study short-term load forecasting (STLF). It aims to predict the day-ahead peak load as well as average 24-h load demand for the city of New Delhi and Greater Noida. Both types of forecasting techniques are compared based on mean-squared logarithmic error (MSLE), mean-squared error (MSE) and mean absolute error (MAE). With the advancement of deep learning techniques, huge amounts of load demand data can be used to forecast the demand from minutes ahead to years ahead.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Samuel I, Ojewola T, Awelewa A, Amaize P (2016) Short-term load forecasting using the time series and artificial neural network methods. IOSR J Electr Electron Eng (IOSR-JEEE). 11(1 Ver. III):72–81. e-ISSN: 2278–1676, p-ISSN: 2320-3331
Mahmoud Elgarhy S, Othman MM, Taha A, Hasanien HM (2017) Short term load forecasting using ANN Technique. In: 19th international middle east power systems conference (MEPCON), Menoufia University, Egypt, 19–21 December (2017)
Singh S, Hussain S, Abid Bazaz M (2017) Short term load forecasting using artificial neural network. In: Fourth international conference on image information processing (ICIIP)
Patel H, Pandya M, Aware M (2015) Short term load forecasting of indian system using linear regression and artificial neural network. In: 5th Nirma University international conference on engineering (NUiCONE)
Lv X, Cheng X, Shuang Y, Yan –mei T (2018) Short-term power load forecasting based on balanced KNN. IOP Conference series: materials science and engineering, vol 322, pp 072058
Gross G, Galiana FD (1987) Short-term load forecasting. The IEEE 75(12):1558–1573
Amjady N (2001) Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans Power Syst 16(3):498–505
Razak FA, Shitan M, Hashim dan AH, Abidin IZ (2009) Load forecasting using time series models. In: Razak FA et al (ed) Jurnal Kejuruteraan, vol 21, pp 53–62
Website link. https://nrldc.in for Delhi city load data
Website link. https://www.wunderground.com for Weather data
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, N., Sharma, P., Kumar, N., Sreejeth, M. (2021). Short-Term Load Forecasting Using Artificial Neural Network and Time Series Model to Predict the Load Demand for Delhi and Greater Noida Cities. In: Mahapatra, R.P., Panigrahi, B.K., Kaushik, B.K., Roy, S. (eds) Proceedings of 6th International Conference on Recent Trends in Computing. Lecture Notes in Networks and Systems, vol 177. Springer, Singapore. https://doi.org/10.1007/978-981-33-4501-0_41
Download citation
DOI: https://doi.org/10.1007/978-981-33-4501-0_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4500-3
Online ISBN: 978-981-33-4501-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)