We address the control of a hybrid energy storage system composed of a lead battery and hydrogen storage. Powered by photovoltaic panels, it feeds a partially islanded building. We aim to minimize building carbon emissions over a long-term period while ensuring that 35% of the building consumption is powered using energy produced on site. To achieve this long-term goal, we propose to learn a control policy as a function of the building and of the storage state using a Deep Reinforcement Learning approach. We reformulate the problem to reduce the action space dimension to one. This highly improves the proposed approach performance. Given the reformulation, we propose a new algorithm, DDPGαrep, using a Deep Deterministic Policy Gradient (DDPG) to learn the policy. Once learned, the storage control is performed using this policy. Simulations show that the higher the hydrogen storage efficiency, the more effective the learning.
Outside temperature is an important quantity in building control. It enables improvement in inhabitant energy consumption forecast or heating requirement prediction. However most previous works on outside temperature forecasting require either a lot of computation or a lot of different sensors. In this paper we try to forecast outside temperature at a multiple hour horizon knowing only the last 24 hours of temperature and computed clear-sky irradiance up to the prediction horizon. We propose the use different neural networks to predict directly at each hour of the horizon instead of using forecast of one hour to predict the next. We show that the most precise one is using one dimensional convolutions, and that the error is distributed across the year. The biggest error factor we found being unknown cloudiness at the beginning of the day. Our findings suggest that the precision improvement seen is not due to trend accuracy improvement but only due to an improvement in precision.
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