Optimal rule based double predictive control for the management of thermal energy in a distributed clean heating system

Solar energy coupled with electric heat storage is a kind of promising energy saving technology for distributed building heating. Precise and quick heat load prediction for the demand side of heating system and renewable energy prediction for the supply side are imperative in realizing the flexibility of building clean energy supply systems. A predictive control based on back propagation (BP) artificial neural network is built to predict the demand heat load of an office building and the heat supply quantity of solar collector (SC) system. The heat dissipation of the SC system is considered to improve the prediction accuracy of its heat supply quantity. A double predictive (DP) control is introduced and combined with dynamic adjustment to optimize the operation of renewable energy, phase change heat storage and valley electric heating system. The results indicate that pre-dictive model is of high precision and the variation coefficient of root mean squared errors corresponding to each prediction parameter are all greater than 0.9. Furthermore, the optimization results indicate that the DP control would save, in March, about 59.3% of charge heat quantity of phase change material (PCM) tank and terms of cost (30.4%). Meanwhile, maintaining indoor temperature between 20 degrees C and 22 degrees C.