Forecasting energy consumption is vital for governments and power distribution companies to prepare for fluctuating demand. We present an analysis of cutting-edge models for prediction in the future with time-series data on energy consumption, encompassing ARIMA, LSTM, and multivariate LSTM. Furthermore, we evaluate the LSTM model with different input and forecasting sizes. After data preprocessing, model training, and forecasting, we rigorously evaluate performance using MSE and RMSE metrics. The multivariate LSTM model gives the best result and minimum MSE and RMSE. This research contributes valuable insights, aiding in efficient resource allocation and informed decision-making for energy management.
REFERENCES
1.
Mudelsee
, Manfred
. “Trend analysis of climate time series: A review of methods
.” Earth-science reviews
190
(2019
): 310
–322
.2.
Varshney
, Sakshi
, and Purwa
Srivastava
. “A Comparative Study of Future Stock Price Prediction Through Artificial Neural Network and ARIMA Modelling
.” NMIMS Management Review
(2024
): 09711023241230367
.3.
Kim
, Kyoung-jae
. “Financial time series forecasting using support vector machines
.” Neurocomputing
55
.1-2
(2003
): 307
–319
.4.
Masini
, Ricardo P.
, Marcelo C.
Medeiros
, and Eduardo F.
Mendes
. “Machine learning advances for time series forecasting
.” Journal of economic surveys
37
.1
(2023
): 76
–111
5.
Casolaro
, Angelo
, et al. “Deep Learning for Time Series Forecasting: Advances and Open Problems
.” Information
14
.11
(2023
): 598
6.
Chen
, Zhiwei
, et al. “Load prediction of integrated energy systems for energy saving and carbon emission based on novel multi-scale fusion convolutional neural network
.” Energy
290
(2024
): 130181
.7.
Zhang
, Xiaobo
, and Jianzhou
Wang
. “A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns
.” Applied Soft Computing
65
(2018
): 478
–494
.8.
Semero
, Yordanos Kassa
, et al. “An accurate very short-term electric load forecasting model with binary genetic algorithm-based feature selection for microgrid applications
.” Electric Power Components and Systems
46
.14-15
(2018
): 1570
–1579
.9.
Ruan
, Yingjun
, et al. “A hybrid model for power consumption forecasting using VMD-based the long short- term memory neural network
.” Frontiers in Energy Research
9
(2022
): 917
.10.
Sun
, Fan
, et al. “Load-forecasting method for IES based on LSTM and dynamic similar days with multi- features
.” Global Energy Interconnection
6
.3
(2023
): 285
–296
.11.
Dickey
, David A.
, and Wayne A.
Fuller
. “Distribution of the estimators for autoregressive time series with a unit root
.” Journal of the American Statistical Association
74
.366a (1979
): 427
–431
.12.
Chujai
, Pasapitch
, Nittaya
Kerdprasop
, and Kittisak
Kerdprasop
. “Time series analysis of household electric consumption with ARIMA and ARMA models
.” Proceedings of the international multiconference of engineers and computer scientists
. Vol. 1. Hong Kong
: IAENG
, 2013
.13.
Suman
, Chanchal
, et al. “Gender Age and Dialect Recognition using Tweets in a Deep Learning Framework- Notebook for FIRE 2019
.” FIRE (Working Notes)
. 2019
14.
Sundaram
, Gaurav
, et al “Biomedical Watermaking Using Arnold Transformation.” Recent Trends in Electronics and Communication: Select Proceedings of VCAS 2020
. Springer
Singapore
, 2022
.15.
Hochreiter
, Sepp
, and Jürgen
Schmidhuber
. “Long short-term memory
.” Neural computation
9
.8
(1997
): 1735
–1780
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