Oludolapo A. Olanrewaju

Energy efficiency assessment using artificial neural network combined with data envelopment analysis

A management technique that uses energy information as a basis to eliminate waste, reduce and control current level of energy use is important to every sector. This paper uses artificial neural network (ANN) and data envelopment analysis (DEA) to examine the effectiveness of energy management. First, the artificial neural network using economic activity, the gross domestic product (GDP), was used to calculate the predicted energy consumption in the industrial sector of South Africa between 1993 and 2000. Data envelopment analysis was then employed to calculate the overall energy efficiency, using the predicted energy consumption as output and the observed energy consumption as input. The results showed that most of the years during this period efficient energy management were successful in achieving energy saving.

Comparison between regression analysis and artificial neural network in project selection

A common problem faced by managers is that of project selection, to decide which project out of the lots should be undertaken. This paper aims at comparing results of the application of two approaches - respectively regression analysis, a parametric method and artificial neural network, a non-parametric technique. To demonstrate these methods, the models were illustrated using Oral, Kettani and Langs data on 37 R&D projects for their success. From statistical analysis, it was discovered that artificial neural network showed superiority to deciding how projects should be ranked and selected.

UNDERSTANDING THE IMPACTS OF GDP AND POPULATION IN SOUTH AFRICA'S ENERGY CONSUMPTION

The objective of this paper is to develop a forecast model dependent on artificial neural network (ANN) to model South Africa’s energy consumption between 2002 and 2009 based on the input factors – gross domestic product (GDP) and population. To what significance does the country’s energy consumption depend on these input factors was also successfully analyzed. The paper employs ANN to carry out these various analyses successfully. In comparison to the regression analyses, it was discovered that ANN is a better modeling technique.

Integrated model for analyzing industrial energy potential

Various studies have proved integrated models to analyze better than single models in the dynamics of energy variables quantification. The present study is a developed integrated Index Decomposition Analyses (IDA), Artificial Neural Network (ANN) and Data Envelopment Analyses (DEA). This model quantifies the industrial sectors possible energy potentials. The considered variables of the study include activity, structure and intensity. Presentation of results to various applications of the model is expressed in this study.

Evaluating GHG components using artificial intelligence: Connection weight approach

The obligation to control the fast increase of emitted greenhouse gas (GHG) for world climate change reduction is the duty of all countries. The significance of the contributing factors to GHG emission, i.e., fuel factor, intensity, economic structure and activity is investigated. Connection weight approach of artificial neural network (ANN) was employed for this study. This paper quantifies the variables responsible for the GHG emissions over the period 1990-2000 in the industrial sectors of Canada. It was discovered that activity effect was the main determinant of the GHG emissions with fuel factor the least significant. The investigation should give a clue to policymakers on how to reduce GHG emissions.

DEA sensitivity analysis on the factors responsible for industrial energy consumption: Case study on the Canadian industrial sector

Energy consumption efficiency is a function of several input variables governing the consumption patterns or behavior. The degree to which efficiency responds to these variables and/or combination thereof will give insight into strategies or ways to better manage energy consumption within an industry. This paper endeavors to evaluate the operation of Canadian industrial sector between year 1990 to 2000, based on data from 15 Canadian industries using Data Envelopment Analysis (DEA). The overall operation is analyzed, and sensitivity analysis is used to appraise the role/relevance of various input factors and their combination thereof.