Abstract
In this paper an optimal controller for battery management in smart home environments is presented in order to save costs and minimize energy waste. The considered scenario includes a load profile that must always be satisfied, a battery-system that is able to storage electrical energy, a photovoltaic (PV) panel, and the main grid that is used when it is necessary to satisfy the load requirements or charge the battery. The optimal controller design is based on a class of adaptive critic designs (ACDs) called action dependent heuristic dynamic programming (ADHDP). Results obtained with this scheme outperform the ones obtained by using the particle swarm optimization (PSO) method.
This work was supported in part by the National Natural Science Foundation of China under Grants 60904037, 60921061, and 61034002, in part by Beijing Natural Science Foundation under Grant 4102061, and in part by China Postdoctoral Science Foundation under Grant 201104162.
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Fuselli, D. et al. (2012). Optimal Battery Management with ADHDP in Smart Home Environments. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_40
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DOI: https://doi.org/10.1007/978-3-642-31362-2_40
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