The Experts below are selected from a list of 50136 Experts worldwide ranked by ideXlab platform
Augustine Mpanda - One of the best experts on this subject based on the ideXlab platform.
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Residential lighting Load Profile modelling: ANFIS approach using weighted and non-weighted data
Energy Efficiency, 2017Co-Authors: O M Popoola, Josiah Munda, Augustine MpandaAbstract:This study involves the use of adaptive neural fuzzy inference system (ANFIS) for residential lighting Load Profile development and evaluation of energy and demand side management (DSM) initiatives. Three variable factors are considered in this study namely, natural light, occupancy (active), and income level. A better correlation of fit and reduced root mean square error was obtained after validation of the developed model using the investigative data—weighted and non-weighted approach (natural lighting). The technique showed that income level of the class in relation to the area (location), working lifestyle of individuals in relation to behavioural pattern, and effect of natural lighting are highly essential and need to be incorporated in any Load Profile development. The generalisation of income needs to be revisited; emerging middle and realised middle-income predictors have shown that their behavioural pattern differs. Forecast based on averages of lamps per households from a survey of an income class to determine lighting usage is prone to high errors. The developed methodology of the ANFIS gives better lighting prediction accuracy in accordance with the learning characteristics of light usage complexities.
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residential lighting Load Profile modelling
Energy and Buildings, 2015Co-Authors: O M Popoola, Augustine Mpanda, Josiah MundaAbstract:Abstract Occupant dynamic presence and characteristics associated with lighting Loads/usage in residential buildings are not replicated in most practices currently adopted in modelling lighting Profile. This study involves the use of adaptive neural fuzzy inference system (ANFIS) for lighting Load Profile prediction. Natural light, occupancy (active) and income level are the characterization (variables) factors considered in this investigation. The accuracy of the developed prediction models in relation to various income earners groups were analyzed using statistical measures; correlation output of the ANFIS approach and the impact of the characteristics on the lighting Profile development in relation to trend analysis were also employed. Results obtained after validation of the developed models using investigative data, metering data and regression model showed a better correlation and root mean square error (RMSE) in comparison with actual values. The intelligence approach showed a better correlation of fit and good learning predictive accuracy in terms of behavioural and environmental variableness; and presents its output according to the complex nature of lighting usage in relation to the variables. The efficacy of the method was also validated.
Tanja Kallio - One of the best experts on this subject based on the ideXlab platform.
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Low-temperature aging mechanisms of commercial graphite/LiFePO4 cells cycled with a simulated electric vehicle Load Profile—A post-mortem study
Journal of energy storage, 2018Co-Authors: Taina Rauhala, Kirsi Jalkanen, Noshin Omar, Tavo Romann, Enn Lust, Tanja KallioAbstract:Abstract Reduced cycle life is one of the issues hindering the adoption of large lithium-ion battery systems in cold-climate countries. Thus, the aging mechanisms of commercial graphite/LiFePO4 (lithium iron phosphate) cells at low temperatures (room temperature, 0 °C and−18 °C) are investigated here through an extended post-mortem analysis. The cylindrical 2.3 Ah cells were cycled with a simulated battery electric vehicle Load Profile, and the aged cells were then disassembled inside an argon-filled glove box. A non-cycled cell was also dismantled as a reference. Half-cell testing was utilized to evaluate the degradation of the electrochemical performance of the electrodes, whereas X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, inductively coupled plasma optical emission spectroscopy and Raman spectroscopy were used to characterize the changes in the materials properties. The full-cell performance loss was mostly seen as capacity fade whereas significant changes in the cell impedance were not observed. Depending on the cycling temperature, loss of cyclable lithium due to solid electrolyte interphase growth and/or lithium plating on the graphite electrode were observed, and they are attributed as the main mechanisms responsible for the capacity loss. Furthermore, increased disordering of the graphite electrode was observed for the cell cycled at −18 °C. The graphite disordering was hypothesized to result from diffusion-induced stress and the mechanical stress caused by severe lithium plating. In contrast, the LiFePO4 electrodes showed only minimal signs of degradation regardless of the cycling temperature.
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low temperature aging mechanisms of commercial graphite lifepo4 cells cycled with a simulated electric vehicle Load Profile a post mortem study
Journal of energy storage, 2018Co-Authors: Taina Rauhala, Kirsi Jalkanen, Tavo Romann, Enn Lust, Tanja KallioAbstract:Abstract Reduced cycle life is one of the issues hindering the adoption of large lithium-ion battery systems in cold-climate countries. Thus, the aging mechanisms of commercial graphite/LiFePO4 (lithium iron phosphate) cells at low temperatures (room temperature, 0 °C and−18 °C) are investigated here through an extended post-mortem analysis. The cylindrical 2.3 Ah cells were cycled with a simulated battery electric vehicle Load Profile, and the aged cells were then disassembled inside an argon-filled glove box. A non-cycled cell was also dismantled as a reference. Half-cell testing was utilized to evaluate the degradation of the electrochemical performance of the electrodes, whereas X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, inductively coupled plasma optical emission spectroscopy and Raman spectroscopy were used to characterize the changes in the materials properties. The full-cell performance loss was mostly seen as capacity fade whereas significant changes in the cell impedance were not observed. Depending on the cycling temperature, loss of cyclable lithium due to solid electrolyte interphase growth and/or lithium plating on the graphite electrode were observed, and they are attributed as the main mechanisms responsible for the capacity loss. Furthermore, increased disordering of the graphite electrode was observed for the cell cycled at −18 °C. The graphite disordering was hypothesized to result from diffusion-induced stress and the mechanical stress caused by severe lithium plating. In contrast, the LiFePO4 electrodes showed only minimal signs of degradation regardless of the cycling temperature.
Taina Rauhala - One of the best experts on this subject based on the ideXlab platform.
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Low-temperature aging mechanisms of commercial graphite/LiFePO4 cells cycled with a simulated electric vehicle Load Profile—A post-mortem study
Journal of energy storage, 2018Co-Authors: Taina Rauhala, Kirsi Jalkanen, Noshin Omar, Tavo Romann, Enn Lust, Tanja KallioAbstract:Abstract Reduced cycle life is one of the issues hindering the adoption of large lithium-ion battery systems in cold-climate countries. Thus, the aging mechanisms of commercial graphite/LiFePO4 (lithium iron phosphate) cells at low temperatures (room temperature, 0 °C and−18 °C) are investigated here through an extended post-mortem analysis. The cylindrical 2.3 Ah cells were cycled with a simulated battery electric vehicle Load Profile, and the aged cells were then disassembled inside an argon-filled glove box. A non-cycled cell was also dismantled as a reference. Half-cell testing was utilized to evaluate the degradation of the electrochemical performance of the electrodes, whereas X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, inductively coupled plasma optical emission spectroscopy and Raman spectroscopy were used to characterize the changes in the materials properties. The full-cell performance loss was mostly seen as capacity fade whereas significant changes in the cell impedance were not observed. Depending on the cycling temperature, loss of cyclable lithium due to solid electrolyte interphase growth and/or lithium plating on the graphite electrode were observed, and they are attributed as the main mechanisms responsible for the capacity loss. Furthermore, increased disordering of the graphite electrode was observed for the cell cycled at −18 °C. The graphite disordering was hypothesized to result from diffusion-induced stress and the mechanical stress caused by severe lithium plating. In contrast, the LiFePO4 electrodes showed only minimal signs of degradation regardless of the cycling temperature.
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low temperature aging mechanisms of commercial graphite lifepo4 cells cycled with a simulated electric vehicle Load Profile a post mortem study
Journal of energy storage, 2018Co-Authors: Taina Rauhala, Kirsi Jalkanen, Tavo Romann, Enn Lust, Tanja KallioAbstract:Abstract Reduced cycle life is one of the issues hindering the adoption of large lithium-ion battery systems in cold-climate countries. Thus, the aging mechanisms of commercial graphite/LiFePO4 (lithium iron phosphate) cells at low temperatures (room temperature, 0 °C and−18 °C) are investigated here through an extended post-mortem analysis. The cylindrical 2.3 Ah cells were cycled with a simulated battery electric vehicle Load Profile, and the aged cells were then disassembled inside an argon-filled glove box. A non-cycled cell was also dismantled as a reference. Half-cell testing was utilized to evaluate the degradation of the electrochemical performance of the electrodes, whereas X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, inductively coupled plasma optical emission spectroscopy and Raman spectroscopy were used to characterize the changes in the materials properties. The full-cell performance loss was mostly seen as capacity fade whereas significant changes in the cell impedance were not observed. Depending on the cycling temperature, loss of cyclable lithium due to solid electrolyte interphase growth and/or lithium plating on the graphite electrode were observed, and they are attributed as the main mechanisms responsible for the capacity loss. Furthermore, increased disordering of the graphite electrode was observed for the cell cycled at −18 °C. The graphite disordering was hypothesized to result from diffusion-induced stress and the mechanical stress caused by severe lithium plating. In contrast, the LiFePO4 electrodes showed only minimal signs of degradation regardless of the cycling temperature.
O M Popoola - One of the best experts on this subject based on the ideXlab platform.
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Residential lighting Load Profile modelling: ANFIS approach using weighted and non-weighted data
Energy Efficiency, 2017Co-Authors: O M Popoola, Josiah Munda, Augustine MpandaAbstract:This study involves the use of adaptive neural fuzzy inference system (ANFIS) for residential lighting Load Profile development and evaluation of energy and demand side management (DSM) initiatives. Three variable factors are considered in this study namely, natural light, occupancy (active), and income level. A better correlation of fit and reduced root mean square error was obtained after validation of the developed model using the investigative data—weighted and non-weighted approach (natural lighting). The technique showed that income level of the class in relation to the area (location), working lifestyle of individuals in relation to behavioural pattern, and effect of natural lighting are highly essential and need to be incorporated in any Load Profile development. The generalisation of income needs to be revisited; emerging middle and realised middle-income predictors have shown that their behavioural pattern differs. Forecast based on averages of lamps per households from a survey of an income class to determine lighting usage is prone to high errors. The developed methodology of the ANFIS gives better lighting prediction accuracy in accordance with the learning characteristics of light usage complexities.
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residential lighting Load Profile modelling
Energy and Buildings, 2015Co-Authors: O M Popoola, Augustine Mpanda, Josiah MundaAbstract:Abstract Occupant dynamic presence and characteristics associated with lighting Loads/usage in residential buildings are not replicated in most practices currently adopted in modelling lighting Profile. This study involves the use of adaptive neural fuzzy inference system (ANFIS) for lighting Load Profile prediction. Natural light, occupancy (active) and income level are the characterization (variables) factors considered in this investigation. The accuracy of the developed prediction models in relation to various income earners groups were analyzed using statistical measures; correlation output of the ANFIS approach and the impact of the characteristics on the lighting Profile development in relation to trend analysis were also employed. Results obtained after validation of the developed models using investigative data, metering data and regression model showed a better correlation and root mean square error (RMSE) in comparison with actual values. The intelligence approach showed a better correlation of fit and good learning predictive accuracy in terms of behavioural and environmental variableness; and presents its output according to the complex nature of lighting usage in relation to the variables. The efficacy of the method was also validated.
Aditya Mahajan - One of the best experts on this subject based on the ideXlab platform.
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Privacy-optimal strategies for smart metering systems with a rechargeable battery
Proceedings of the American Control Conference, 2016Co-Authors: Simon Li, Ashish Khisti, Aditya MahajanAbstract:In smart-metered systems, fine-grained power demand data (Load Profile) is communicated from a user to the utility provider. The correlation of the Load Profile with a user's private activities leaves open the possibility of inference attacks. Using a rechargeable battery, the user can partially obscure its Load Profile and provide some protection to the private information using various battery charging policies. Using the mutual information as the privacy metric we study a class of optimal battery charging policies. When the power demand is a first-order Markov process, we propose a series of reductions on the optimization problem and ultimately recast it as a Markov decision process. In the special case of i.i.d. demand, we explicitly characterize the optimal policy and show that the associated privacy-leakage can be expressed as a single-letter mutual information expression.
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structure of optimal privacy preserving policies in smart metered systems with a rechargeable battery
International Workshop on Signal Processing Advances in Wireless Communications, 2015Co-Authors: Simon Li, Ashish Khisti, Aditya MahajanAbstract:In smart-metered systems, fine-grained time-series power usage data (Load Profile) is communicated from a user to the utility provider. The correlation of the Load Profile with a user's private activities leaves open the possibility of inference attacks. Using a rechargeable battery, the user can partially obscure its Load Profile and provide some protection to the private information using various strategies for charging and discharging the battery (battery management policies). Using mutual information as the privacy metric, we study optimal battery management policies for discrete alphabets. We show that the problem can be formulated as a Markov Decision Process, identify the associated state and action space, and using this framework characterize the optimal policy for the binary alphabet case.
