Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting

Rajbhandari, Yaju; Marahatta, Anup; Ghimire, Bishal; Shrestha, Ashish; Gachhadar, Anand; Thapa, Anup; Chapagain, Kamal; Korba, Petr

doi:10.3390/asi4030043

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25906

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting
Authors:	Rajbhandari, Yaju Marahatta, Anup Ghimire, Bishal Shrestha, Ashish Gachhadar, Anand Thapa, Anup Chapagain, Kamal Korba, Petr
et. al:	No
DOI:	10.3390/asi4030043 10.21256/zhaw-25906
Published in:	Applied System Innovation
Volume(Issue):	4
Issue:	3
Page(s):	43
Issue Date:	2021
Publisher / Ed. Institution:	MDPI
ISSN:	2571-5577
Language:	English
Subjects:	Energy consumption; Short-term electricity demand forecasting; Feedforward neural network; Temperature impact on electricity demand
Subject (DDC):	333.79: Energy
Abstract:	Short-term electricity demand forecasting is one of the best ways to understand the changing characteristics of demand that helps to make important decisions regarding load flow analysis, preventing imbalance in generation planning, demand management, and load scheduling, all of which are actions for the reliability and quality of that power system. The variation in electricity demand depends upon various parameters, such as the effect of the temperature, social activities, holidays, the working environment, and so on. The selection of improper forecasting methods and data can lead to huge variations and mislead the power system operators. This paper presents a study of electricity demand and its relation to the previous day’s lags and temperature by examining the case of a consumer distribution center in urban Nepal. The effect of the temperature on load, load variation on weekends and weekdays, and the effect of load lags on the load demand are thoroughly discussed. Based on the analysis conducted on the data, short-term load forecasting is conducted for weekdays and weekends by using the previous day’s demand and temperature data for the whole year. Using the conventional time series model as a benchmark, an ANN model is developed to track the effect of the temperature and similar day patterns. The results show that the time series models with feedforward neural networks (FF-ANNs), in terms of the mean absolute percentage error (MAPE), performed better by 0.34% on a weekday and by 8.04% on a weekend.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25906
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Energy Systems and Fluid Engineering (IEFE)
Appears in collections:	Publikationen Life Sciences und Facility Management

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Rajbhandari, Y., Marahatta, A., Ghimire, B., Shrestha, A., Gachhadar, A., Thapa, A., Chapagain, K., & Korba, P. (2021). Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting. Applied System Innovation, 4(3), 43. https://doi.org/10.3390/asi4030043

Rajbhandari, Y. et al. (2021) ‘Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting’, Applied System Innovation, 4(3), p. 43. Available at: https://doi.org/10.3390/asi4030043.

Y. Rajbhandari et al., “Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting,” Applied System Innovation, vol. 4, no. 3, p. 43, 2021, doi: 10.3390/asi4030043.

RAJBHANDARI, Yaju, Anup MARAHATTA, Bishal GHIMIRE, Ashish SHRESTHA, Anand GACHHADAR, Anup THAPA, Kamal CHAPAGAIN und Petr KORBA, 2021. Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting. Applied System Innovation. 2021. Bd. 4, Nr. 3, S. 43. DOI 10.3390/asi4030043

Rajbhandari, Yaju, Anup Marahatta, Bishal Ghimire, Ashish Shrestha, Anand Gachhadar, Anup Thapa, Kamal Chapagain, and Petr Korba. 2021. “Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting.” Applied System Innovation 4 (3): 43. https://doi.org/10.3390/asi4030043.

Rajbhandari, Yaju, et al. “Impact Study of Temperature on the Time Series Electricity Demand of Urban Nepal for Short-Term Load Forecasting.” Applied System Innovation, vol. 4, no. 3, 2021, p. 43, https://doi.org/10.3390/asi4030043.