Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic Algorithms

Jeng-Fung Chen,Thi Doan,Quang Do,Thi Nguyen

doi:10.3390/info9030051

Jeng-Fung Chen, Thi Doan + Show 2 more

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https://doi.org/10.3390/info9030051

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Abstract

Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS) is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA), Cuckoo Optimization Algorithm (COA), and Cuckoo Search (CS) are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS) optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR). In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

Highlights

Electric energy plays a fundamental role in business operations all over the world
This study presents a novel approach that combines the Haar wavelet transform and the heuristic algorithms into the neuro-fuzzy system
For wavelet Gravitational Search Algorithm (GSA)-hierarchical adaptive neuro-fuzzy inference system (ANFIS) (HANFIS) and GSA HANFIS, the parameters for the GSA algorithm were as follows: the number of initial population was 20 and the gravitational constant in Equation (12) was determined by the function G(t) = G0 exp(−αt/T), where G0 = 100, α = 20, and T was the total number of iterations

Summary

Introduction

Electric energy plays a fundamental role in business operations all over the world. Industries, homes, and services worldwide depend on efficient, reliable, and accessible electricity. As electricity has a deep impact on economic activities, the management of electricity and electricity sources must be carefully implemented to guarantee the efficient use of electricity. The key to this is high-quality capacity planning, scheduling, and operations of the electric power systems. An accurate knowledge of future electricity demands is necessary for solid capacity planning and scheduling. This leads to a need for reliable electricity demand forecasting to guarantee that electricity generation is sufficient for demand. Demand forecasting is Information 2018, 9, 51; doi:10.3390/info9030051 www.mdpi.com/journal/information

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