###### Abstract  
The large\(-\)scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)\(-\)reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA-BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively.  
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large power grid. Now, it has gradually become a vital and effective method to solve the disadvantages of the power system in many countries.  
The ESSs are the core of the microgrid. These can guarantee power quality and microgrid reliability as well as reduce energy loss. Among all kinds of energy storage technologies, battery energy storage systems (BESSs) have gradually become a very attractive and prominent technology [12]. Its versatility, fast response speed, high energy density, and high efficiency are the main reasons. By absorbing power from the grid during off-peak hours and supplying it during peak hours, a BESS enables peak shifting/shaving, improves power quality, and alleviates congestion. As a result, various types of BESS are becoming increasingly integrated into modern energy systems. However, despite continuous advances in electrochemical technology, the management and control of BESS remain challenging problems [10].  
The problem of how effectively maintaining the power generation and consumption balance impacts the stable operation of the microgrid [12]. If the network topology or load of the microgrid changes, appropriate control strategies must be adopted for the power generation device to ensure its safe and reliable operation [13; 14]. The output of wind power, photo-voltaic, and other micro-power sources in the microgrid is very unstable, and requires the coordination and cooperation of all DERs [15]. Meanwhile, load forecasting, renewable power generation prediction, and electricity price forecasting are also important factors affecting the performance of the management system in the microgrid [5]. Therefore, it is an important prerequisite for realizing the intelligent management of microgrids [16]. If the load, power, and electricity price of the microgrid can be relatively accurately predicted, the stability and power quality of the power system can be guaranteed.