Hybrid intelligent strategy for multifactor influenced electrical energy consumption forecasting

ABSTRACT

This paper proposes a novel hybrid strategy based on intelligent approaches to forecast electricity consumptions. The proposed forecasting strategy includes three main steps: (a) the evaluation of a correlation coefficient for socio-economic indicators on electric energy consumptions, (b) the classification of historical and socio-economic indicators using the proposed feature selection method, (c) the development of a new combined method using Adaptive Neuro-Fuzzy Inference System and Whale Optimization Algorithm to predict electrical energy consumptions. The simulation results have been tested and validated by real data sets achieved within 1992 and 2010 in two pilot cases in a developing country (Iran) and a developed one (Italy). The research findings pinpointed the greater accuracy and stability of the new developed method for electrical energy consumption forecasting compared to existing single and hybrid benchmark models.

KEYWORDS:

• Electrical energy consumption
• long-term forecasting
• socio-economic indicators
• ANFIS
• whale optimization

Nomenclatures

EEC	=	Electric Energy Consumption
P	=	Population
GDP	=	Gross Domestic Product
SI	=	Stock Index
EMP	=	Energy Export
IMP	=	Energy Import
IR	=	Inflation Rate
ANN	=	Artificial Neural Network
PSO	=	Particle Swarm Optimization
GA	=	Genetic Algorithm
ACO	=	Ant Colony Optimization
RBF	=	Radial Basis Function neural network