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

Full metadata record

DC Field	Value	Language
dc.contributor.author	Rajbhandari, Yaju	-
dc.contributor.author	Marahatta, Anup	-
dc.contributor.author	Ghimire, Bishal	-
dc.contributor.author	Shrestha, Ashish	-
dc.contributor.author	Gachhadar, Anand	-
dc.contributor.author	Thapa, Anup	-
dc.contributor.author	Chapagain, Kamal	-
dc.contributor.author	Korba, Petr	-
dc.date.accessioned	2022-10-28T13:38:35Z	-
dc.date.available	2022-10-28T13:38:35Z	-
dc.date.issued	2021	-
dc.identifier.issn	2571-5577	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/25906	-
dc.description.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.	de_CH
dc.language.iso	en	de_CH
dc.publisher	MDPI	de_CH
dc.relation.ispartof	Applied System Innovation	de_CH
dc.rights	http://creativecommons.org/licenses/by/4.0/	de_CH
dc.subject	Energy consumption	de_CH
dc.subject	Short-term electricity demand forecasting	de_CH
dc.subject	Feedforward neural network	de_CH
dc.subject	Temperature impact on electricity demand	de_CH
dc.subject.ddc	333.79: Energie	de_CH
dc.title	Impact study of temperature on the time series electricity demand of urban Nepal for short-term load forecasting	de_CH
dc.type	Beitrag in wissenschaftlicher Zeitschrift	de_CH
dcterms.type	Text	de_CH
zhaw.departement	Life Sciences und Facility Management	de_CH
zhaw.organisationalunit	Institut für Energiesysteme und Fluid-Engineering (IEFE)	de_CH
dc.identifier.doi	10.3390/asi4030043	de_CH
dc.identifier.doi	10.21256/zhaw-25906	-
zhaw.funding.eu	No	de_CH
zhaw.issue	3	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.start	43	de_CH
zhaw.publication.status	publishedVersion	de_CH
zhaw.volume	4	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
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.