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A combination approach based on seasonal adjustment method and echo state network for energy consumption forecasting in USA

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Abstract

It is extremely significant to construct a scientific and accurate forecasting model for energy consumption of the USA, because it could help to formulate energy policies and allocate energy resources. In recent years, more and more hybrid models based on divided-and-conquer method have been applied in energy consumption prediction to obtain satisfactory results. However, owning to the obvious enhancing effect of decomposition methods, the issue concerning seasonal fluctuation existed in time series is rarely considered before modeling. It is a fact that seasonality indeed influences the performance of prediction. This paper proposes a hybrid forecasting model for energy consumption, which combines seasonal adjustment method, ensemble empirical mode decomposition (EEMD), echo state network (ESN), and grasshopper optimization algorithm (GOA). The seasonal adjustment method that inherits the idea of divide and conquer is used to decompose original time series into only two parts including seasonal subseries and remainder subseries rather than regular three parts(seasonality, trend, and residual) for avoiding the complex modeling task of the residual subseries. Then models ESN and EEMD-GOA-ESN are utilized to model and predict the seasonal subseries and remainder subseries respectively. Finally, two parts are summed to generate the final predictive results. The empirical studies of fossil fuels, nuclear electric power, and renewable energy consumptions show that the proposed model outperforms other alternative benchmarks with regard to effectiveness and scalability. Besides, the sample extrapolation forecasting displays that the technique could limit the error of monthly energy consumption to 3.3%.

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Abbreviations

ANN:

Artificial neural network

LSTM:

Long short-term memory

ARIMA:

Autoregressive integrated moving average

MAE:

Mean absolute error

BPNN:

Back propagation neural network

MAPE:

Mean absolute percentage error

CEEMD:

Complete ensemble empirical mode decomposition MLR multiple linear regression

DE:

Differential evolution

MNN:

Multilayer neural network

EEMD:

Ensemble empirical mode decomposition

NMGM:

Nonlinear metabolic grey model

ELM:

Extreme learning machine

NN:

Neural network

EMD:

Empirical mode decomposition

PSO:

Particle swarm optimization

ENN:

Elman neural network

RF:

Random forest

ESM:

Exponential smoothing method

RMSE:

Root mean square error

ESN:

Echo state network

RNN:

Recurrent neural network

GA:

Genetic algorithm

SARIMA:

Seasonal autoregressive integrated moving average

GM:

Grey model

SNN:

Spiking neural networks

GOA:

Grasshopper optimization algorithm

STL:

Seasonal trend decomposition procedures based on loess

GWO:

Grey wolf optimization

SVR:

Support vector regression

IMF:

Intrinsic mode function

WOA:

Whale optimization algorithm

LSSVR:

Least square support vector regression

XGB:

Extreme gradient boosting

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Authors and Affiliations

  1. School of Mathematics and Statistics, Lanzhou University, Lanzhou, 730000, China

    Lan Qin

  2. School of Mathematics and Statistics, Center for Data Science, Laboratory of Applied Mathematics and Complex Systems, Lanzhou University, Lanzhou, 730000, China

    Weide Li

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  1. Lan Qin
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  2. Weide Li
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Qin, L., Li, W. A combination approach based on seasonal adjustment method and echo state network for energy consumption forecasting in USA. Energy Efficiency 13, 1505–1524 (2020). https://doi.org/10.1007/s12053-020-09897-x

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