气候变化领域集成服务门户(CCPortal): A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

CCPortal

DOI	10.1016/j.enpol.2022.113097
	A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India
	Chaturvedi, Shobhit; Rajasekar, Elangovan; Natarajan, Sukumar; McCullen, Nick
发表日期	2022
ISSN	0301-4215
EISSN	1873-6777
卷号	168 页码:13
英文摘要	Selecting a suitable energy demand forecasting method is challenging due to the complex interplay of long-term trends, short-term seasonalities, and uncertainties. This paper compares four time-series models performance to predict total and peak monthly energy demand in India. Indian's Central Energy Authority's (CEA) existing trend-based model is used as a baseline against (i) Seasonal Auto-Regressive Integrated Moving Average (SARIMA), (ii) Long Short Term Memory Recurrent Neural Network (LSTM RNN) and (iii) Facebook (Fb) Prophet models. Using 108 months of training data to predict 24 months of unseen data, the CEA model performs well in predicting monthly total energy demand with low root-mean square error (RMSE 4.23 GWh) and mean absolute percentage error (MAPE, 3.4%), but significantly under predicts monthly peak energy demand (RMSE 13.31 GW, MAPE 7.2%). In contrast, Fb Prophet performs well for monthly total (RMSE 4.23 GWh, MAPE 3.3%) and peak demand (RMSE 6.51 GW, MAPE 3.01%). SARIMA and LSTM RNN have higher prediction errors than CEA and Fb Prophet. Thus, Fb Prophet is selected to develop future energy forecasts from 2019 to 2024, suggesting that India's annual total and peak energy demand will likely increase at an annual growth rate of 3.9% and 4.5%, respectively.
英文关键词	Energy demand forecasting; SARIMA; LSTM RNN; Fb prophet
学科领域	Economics; Energy & Fuels; Environmental Sciences; Environmental Studies
语种	英语
WOS研究方向	Business & Economics ; Energy & Fuels ; Environmental Sciences & Ecology
WOS记录号	WOS:000828205300002
来源期刊	ENERGY POLICY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/272283
作者单位	Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Roorkee; Pandit Deendayal Energy University; University of Bath
推荐引用方式 GB/T 7714	Chaturvedi, Shobhit,Rajasekar, Elangovan,Natarajan, Sukumar,et al. A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India[J],2022,168:13.
APA	Chaturvedi, Shobhit,Rajasekar, Elangovan,Natarajan, Sukumar,&McCullen, Nick.(2022).A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India.ENERGY POLICY,168,13.
MLA	Chaturvedi, Shobhit,et al."A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India".ENERGY POLICY 168(2022):13.