Abstract

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.

Highlights

  • Electrical energy consumption forecasting is imperative for efficient energy management in the supply and demand sector of the smart grid (SG) [1]

  • The results clearly indicate that the proposed FS-factored conditional restricted Boltzmann machine (FCRBM)-genetic wind driven optimization (GWDO) framework forecasts the day-ahead electrical energy consumption of the FE power grid

  • Electrical energy consumption forecasting is imperative for the decision-making activities of the SG such as efficient use of available energy, operation planning, load scheduling, and contract evaluation

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Summary

A Novel Accurate and Fast Converging Deep

Ghulam Hafeez 1,2 , Khurram Saleem Alimgeer 1 , Zahid Wadud 3 , Zeeshan Shafiq 2 , Mohammad Usman Ali Khan 4 , Imran Khan 2 , Farrukh Aslam Khan 5, *. Mobile and Internet Services in Ubiquitous Computing (IMIS-2019), Sydney, NSW, Australia, 3–5 July 2019; pp. Advances in Intelligent Systems and Computing, volume 994.

Introduction
Related Work
Limitations
The Proposed Deep Learning-Based Hybrid Model
Section 3.4. Fourth module
Redundancy Filter Operation
Features Interaction Operation Session
The Modified Feature Selection Technique
A Deep Learning FCRBM Model Based Forecasting Module
The Proposed GWDO Algorithm-Based Optimization Module
Utilization Module for Forecasting Results
Proposed
Day Ahead Electrical Energy Consumption Forecasting With Hour Resolution
Findings
Conclusions

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