An Innovative Model for Short-term Electric Load Forecasting in Smart Grid

Accurate electric load forecasting is important due to its application in the decision making and operation of the power grid. However, the electric load profile is a complex signal due to non-linear and stochastic behavior of consumers. Despite much research conducted in this area; still accurate f

Project Title

An Innovative Model for Short-term Electric Load Forecasting in Smart Grid

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Accurate electric load forecasting is important due to its application in the decision making and operation of the power grid. However, the electric load profile is a complex signal due to non-linear and stochastic behavior of consumers. Despite much research conducted in this area; still accurate forecast models are needed. In this article, a short-term electric load forecasting model is proposed. The proposed model is a hybrid model composed of data pre-processing and feature selection module, training and forecasting module, and an optimization module. The data pre-processing and feature selection module is based on modified mutual information (MMI) technique, which is an improved version of mutual information technique, used to select abstractive features from historical data. The training and forecasting module is based on factored conditional restricted Boltzmann machine (FCRBM), which is a deep learning model, enabled via learning to forecast the future electric load. The optimization module is based on our proposed genetic wind driven (GWDO) optimization algorithm, which is used to fine tune the adjustable parameters of the model. {The accuracy of the proposed framework is evaluated by means of historical data of three USA power grid stations, taken from publicly available PJM electricity market.} The model is also compared with some recent models like: Bi-level, mutual information based artificial neural network (MI-ANN), ANN based accurate and fast converging (AFC-ANN), and long short-term memory (LSTM).

Project Objectives

Forecast accuracy improvement

Convergence rate improvement for efficient energy management in Smart Grid

Reduce the error of a machine learning

Reduce the execution time of machine

Project Implementation Method

Python and

MATLAB 

Benefits of the Project

These models have many applications in the day-to-day operations of electric utilities such as energy generation planning, load switching, energy purchasing, infrastructure maintenance, and contract evaluation.

Technical Details of Final Deliverable

Accurate forecasting of power system short -term load has been one of the most important issues in the electricity industry. The improved DBN is applied to short-term load forecasting, empirical studies show that the method has a high prediction accuracy and faster computing speed. 

Final Deliverable of the Project

Software System

Core Industry

Energy

Other Industries

Energy

Core Technology

Others

Other Technologies

Clean Tech

Sustainable Development Goals

Affordable and Clean Energy

Required Resources

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