• Energy Research

In this study, soft computing methods are designed and adapted to estimate energy consumption of the building according to main building envelope parameters such as material thicknesses and insulation K-value. In order to predict the building energy consumption, novel intelligent soft computing schemes, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) are used. The polynomial, linear, and radial basis function (RBF) is applied as the kernel function of the SVR to estimate the optimal energy consumption of buildings. The performance of proposed optimizers is confirmed by simulation results. The SVR results are compared with the ANFIS, artificial neural network (ANN), and genetic programming (GP) results. The computational results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach in comparison to the SVR estimation. Based on the simulation results, the effectiveness of the proposed optimization strategies is verified. The data used in soft computing were obtained from 180 simulations in EnergyPlus for variations of building envelope parameters.

In this study, soft computing methods are designed and adapted to estimate energy consumption of the building according to main building envelope parameters such as material thicknesses and insulation K-value. In order to predict the building energy consumption, novel intelligent soft computing schemes, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) are used. The polynomial, linear, and radial basis function (RBF) is applied as the kernel function of the SVR to estimate the optimal energy consumption of buildings. The performance of proposed optimizers is confirmed by simulation results. The SVR results are compared with the ANFIS, artificial neural network (ANN), and genetic programming (GP) results. The computational results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach in comparison to the SVR estimation. Based on the simulation results, the effectiveness of the proposed optimization strategies is verified. The data used in soft computing were obtained from 180 simulations in EnergyPlus for variations of building envelope parameters.

Rapidly rising trends of fuel consumption indicate enormous energy crisis of global proportions in near future. Following the trend, Malaysia’s fuel consumption has been increasing by an annual rate of 7.2% since 1990 and has even reached 44.9 Mtoe in 2008. It is forecasted to reach 207.3 Mtoe by the year 2030. Due to serious depletion of reserves in various onshore locations, the exploration process is expanded to offshore deeper waters. Seven sedimentary basins belonging to Malaysia, in South China Sea, show great promise to be excellent sources of hydrocarbons. For deep-sea exploration fixed offshore structures are not feasible. An economical alternative is Spar platforms, which are floating structures ideal for exploration of deep water deposits. In this research, Malaysian experience in offshore hydrocarbon exploration is investigated. Various kinds of operational Spar platforms are censoriously explored and their recent technical developments are reviewed. The study reveals that Malaysia’s primary energy requirements were met (in year 2008) with natural gas by 43.4% of the total, crude oil by 38.2%, coal by 15.3% and hydropower by 3.1%; indicating evidently that natural gas and crude oil are still the predominant energy sources. Out of the total energy, around 70% oil and 85% natural gas come from offshore fields. These large figures highlight the necessity to consider economically viable alternatives. Spar platform is an innovative marine structure designed to conduct such deep sea explorations. First commissioned Spar at Kikeh field of Malaysia is testimony to immense potential and possibilities of incorporating Spar platforms in the country’s deep reserves for sustainable energy generation. Classic Spar, Truss Spar, Cell Spar and Cell–truss Spar are identified to be well suited for these environments. Since the offshore fields are located at waters with more than 1000 m depth, Spar platforms can be successfully installed at these Malaysian deep water fields.

Rapidly rising trends of fuel consumption indicate enormous energy crisis of global proportions in near future. Following the trend, Malaysia’s fuel consumption has been increasing by an annual rate of 7.2% since 1990 and has even reached 44.9 Mtoe in 2008. It is forecasted to reach 207.3 Mtoe by the year 2030. Due to serious depletion of reserves in various onshore locations, the exploration process is expanded to offshore deeper waters. Seven sedimentary basins belonging to Malaysia, in South China Sea, show great promise to be excellent sources of hydrocarbons. For deep-sea exploration fixed offshore structures are not feasible. An economical alternative is Spar platforms, which are floating structures ideal for exploration of deep water deposits. In this research, Malaysian experience in offshore hydrocarbon exploration is investigated. Various kinds of operational Spar platforms are censoriously explored and their recent technical developments are reviewed. The study reveals that Malaysia’s primary energy requirements were met (in year 2008) with natural gas by 43.4% of the total, crude oil by 38.2%, coal by 15.3% and hydropower by 3.1%; indicating evidently that natural gas and crude oil are still the predominant energy sources. Out of the total energy, around 70% oil and 85% natural gas come from offshore fields. These large figures highlight the necessity to consider economically viable alternatives. Spar platform is an innovative marine structure designed to conduct such deep sea explorations. First commissioned Spar at Kikeh field of Malaysia is testimony to immense potential and possibilities of incorporating Spar platforms in the country’s deep reserves for sustainable energy generation. Classic Spar, Truss Spar, Cell Spar and Cell–truss Spar are identified to be well suited for these environments. Since the offshore fields are located at waters with more than 1000 m depth, Spar platforms can be successfully installed at these Malaysian deep water fields.

Abstract The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.

Abstract The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN.

Abstract This article presents the analysis of the structure, energy and cost efficiency of three lightweight structural systems – wood light frames (WLF), lightweight steel frames (LGSF) and 3D sandwich (3DSP) panels – during their useful life. The structural systems focussed upon in this study are commonly used in Eastern Europe with specific reference to Turkey. The structural analysis and design was carried out using ETABS while EnergyPlus was used in the analysis of the energy consumption of the buildings. The results of the structural analysis of the three alternative construction systems show that 3DSP has better structural behaviour in terms of resistance against lateral loads. The thermal performance evaluation of the walls and ceilings shows that the WLF and LGSF walls have better insulation values (12.5% lower U-value) while the roof construction of the 3DSP has much better insulation performance (70% lower U-value). Moreover, the building designed with 3DSP requires 11% less energy for total heating and cooling during one year. The information for the building industry in Turkey shows that the cost of construction for 3DSP construction is 34.6% lower than for WLF and 27.7% lower than LGSF.

Abstract This article presents the analysis of the structure, energy and cost efficiency of three lightweight structural systems – wood light frames (WLF), lightweight steel frames (LGSF) and 3D sandwich (3DSP) panels – during their useful life. The structural systems focussed upon in this study are commonly used in Eastern Europe with specific reference to Turkey. The structural analysis and design was carried out using ETABS while EnergyPlus was used in the analysis of the energy consumption of the buildings. The results of the structural analysis of the three alternative construction systems show that 3DSP has better structural behaviour in terms of resistance against lateral loads. The thermal performance evaluation of the walls and ceilings shows that the WLF and LGSF walls have better insulation values (12.5% lower U-value) while the roof construction of the 3DSP has much better insulation performance (70% lower U-value). Moreover, the building designed with 3DSP requires 11% less energy for total heating and cooling during one year. The information for the building industry in Turkey shows that the cost of construction for 3DSP construction is 34.6% lower than for WLF and 27.7% lower than LGSF.

Abstract Energy efficiency is the predominant criterion in green building indices, which, in turn, contributes to sustainable development. One of the materials commonly used in the insulation of buildings is foamed concrete. This investigation presents the main objective of the experimental results concerning the thermal conductivity of oil palm shell foamed geopolymer concrete (OPSFGC), utilizing waste materials such as low-calcium fly ash (FA) and palm oil fuel ash (POFA) as cementitious materials, and oil palm shell (OPS) as lightweight coarse aggregate (LWA). Three OPSFGC mixtures with densities of 1300, 1500 and 1700 kg/m3 were prepared using an artificial foaming agent; a control mix without foam and conventional materials – block and brick – were used for comparison. The test results on the mechanical and transport properties are also discussed. The thermal conductivity of OPSFGC13 of about 0.47 W/mK was 22% and 48% lower than the conventional wall materials, block and brick, respectively. OPSFGC, with a density of 1300 and 1500 kg/m3, could be categorized as structural and insulating concrete, Class-II, whereas OPSFGC with a density of 1700 kg/m3 is classified as Class-I structural grade concrete with a compressive strength and thermal conductivity of about 30 MPa and 0.58 W/mK, respectively.