Abstract: It is important for crop growth to maintain suitable temperature in plant factory, however large heating energy consumption has been proved to be an obstacle that restricts its development. In Europe, the cost of heating accounts for about 30% of the total operation cost during the winter, but in the north of latitude 43° of China, the proportion reaches 60% to 70%. The traditional heating equipment such as coal-fired boilers has an energy utilization rate of only 40% to 50%. So it is very necessary to apply renewable energy to plant factory. Regulating heating modes by tracking and predicting the heating load changes in plant factory is the key to achieve energy saving. Because of the high energy consumption in winter, accurate heating load prediction can improve the energy saving effect of groundwater source heat pump with water energy storage. Changes of heating load in natural light plant factory are dynamic, time-varying, highly turbulent and uncertain. Artificial neural networks is ideal for predicting load changes, especially BP (back propagation) neural network has strong nonlinear mapping ability, which is generally used by many scholars for building heating load prediction, but rarely in plant factory. Given that heating load of both plant factory and building have nonlinear characteristics, we used BP neural network to predict the next day's heating load of plant factory to promote energy-saving control optimization. The BP neural network model has three levels: input layer, hidden layer and output layer. Input parameters include indoor and outdoor air temperature, solar radiation intensity, indoor relative humidity, indoor absolute humidity, indoor wind speed, etc. For plant factory, the next day's weather condition has a significant impact on the heating load. The output variable is determined as the next day's hourly glass greenhouse load value. The number of neurons in the input layer was 9, the number of neurons in the hidden layer was 13, the selected layer number of hidden layers was 1, the learning rate was 0.25 to 0.30, and the initial momentum factor was 0.9. Common evaluation indicators used to determine whether the neural network converges, included standard deviation, coefficient of variation, and expected error percentage. After algorithm steps being determined, the next day's heating load was predicted based on reasonable algorithmic procedures and steps. Experimental data in the paper was obtained from a natural light plant factory powered by groundwater source heat pump with water energy storage system in Chongming National Facility Agricultural Engineering Technology Research Center. Using the neural network toolbox of Matlab to train and simulate the model to process the experimental data from January 19th to 28th, the value of the error function was 0.002 999 94 which was less than the set value of 0.003, so the neural network was convergent. Prediction effect can be drawn by comparison between the actual surveyed value and the predicted value of the heating load. The main heating load was concentrated on 0:00-6:00 and 17:00-24:00 o’clock in the plant factory, and most of these periods were in the cheap electricity price period of Shanghai. Adjusting the operating strategy and operating mode of the energy supply system were based on the predicted heating load，the heat pump operated at full load or high load during the period of cheap electricity prices, and excess heat was stored in the hot storage tank. The hot storage tank provided heat to plant factory during the period of moderate and expensive electricity price. In this case, the energy cost would be reduced. Therefore, it was significantly economical to control start-stop time of the groundwater source heat pump with water energy storage for plant factory heating project. The error was controlled within ±6% basically between the actual value and the predicted value of the heating loads. Therefore, the results showed that the BP neural network was suitable for the next day's heating load prediction of plant factory.