Conceptual Framework of Modelling for Malaysian Household Electrical Energy Consumption using Artificial Neural Network based on Techno-Socio Economic Approach

Abstract

The residential sector was one of the contributors to the increase in the world energy consumption and CO2 emission due to the increase population, economic development, and improved living standard. Developing a reliable model of electrical energy consumption based on techno-socio economic factors was challenging since many assumptions need to be considered. Over the past decade, bottom-up approaches such as multi-linear regression, artificial neural network (ANN), and conditional demand analysis were used for developing mathematical models to investigate interrelated characteristics among techno-socio economic factors. However, the existing models mostly were focused on countries that had different socio-economic level and cultures from the developing countries of the Association of Southeast Asian Nations. Similar studies in that tropical region were very scarce and only limited for linear modelling under the conditions of techno-socio economic factors. In this study, we proposed ANN for developing a model of electrical energy consumption based on techno-socio economic factors for a tropical region, Malaysia. In order to develop the model, quantitative measurement and qualitative assessment were required. The quantitative measurement was based on the monitoring of total electrical energy consumption with a one-minute interval. In contrast, the qualitative assessment utilized a questionnaire survey to assess household characteristics based on techno-socio economic parameters. The objective of this paper was to propose a conceptual framework of the estimation model for household electrical energy consumption with the consideration of techno-socio economic factors using ANN.

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

“…Previous studies employed ANNs to predict the building load based on the physical characteristics, environmental factors, or building use schedule. Sena et al proposed that the behavior and characteristics of users and physical elements of buildings should be predicted through ANN but their approach could not be implemented [40]. The present study employed the demographic, social, and economic characteristics of building residents in an ANN to predict user-based energy consumption.…”

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

“…Swarno et al [23] observed the diurnal variation of the urban microclimate in Kuala Lumpur, and indicated that urban microclimatic parameters were influenced by monsoon seasons and the urban topography. Sena et al [24] constructed a conceptual framework of electrical energy consumption modeling for Malaysian households, using an artificial neural network. They suggested that the combination of sociodemographics, house characteristics, occupant behavior, and appliance characteristics were important in developing an ANN model for estimating electrical energy consumption.…”

Proposed study on evaluating and forecasting the resident property value based on specific determinants by case base reasoning and artificial neural network approach

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