Autonomous off-grid systems dependent upon Renewable Energy (RE) sources are characterized by stochastic supplies of the fluctuating low short-circuit power. Power Quality (PQ) standards define load characteristics in electric power systems and their ability to function properly without failures. Monitoring, prediction and optimization of PQ parameters are necessary to maintain their alterations steady within the prescribed range, which allow fault-tolerant operation of various electrical devices. It is not possible to measure complete PQ data for all possible combinations of dozens of grid-connected appliances, whose load specifics and collisions primarily determine the course of PQ parameters and their eventual disturbances. Self-adapting PQ prediction models based on Artificial Intelligence (AI) are required as induced power is influenced particularly by changeable weather conditions in real off-grid operation mode of systems using RE. A novel multi-step PQ prediction algorithm is proposed, which develops AI models with the gradually increasing number of selected input PQ-parameters. In each next step a more complex model is formed, using an additional co-related PQ-input to calculate its target PQ-output with a better accuracy. PQ-models with the progressively growing PQ-inputs, using their data predicted in the previous step, can better approximate and estimate the target quantity. The presented results show this training and feature selection procedure can step by step improve accuracy of PQ-models for unknown combinations of off-grid connected household appliances.

依赖于可再生能源（RE）的自主离网系统的特点是，随机供应的低短路功率波动很大。电能质量（PQ）标准定义了电力系统中的负载特性及其正常运行而不会发生故障的能力。必须对PQ参数进行监视，预测和优化，以将其更改稳定地维持在规定的范围内，从而允许各种电气设备进行容错操作。无法测量数十种并网设备的所有可能组合的完整PQ数据，这些设备的负载特性和冲突主要决定了PQ参数的变化过程以及最终的干扰。需要使用基于人工智能（AI）的自适应PQ预测模型，因为在使用RE的系统的实际离网运行模式下，感应功率尤其受到天气条件变化的影响。提出了一种新颖的多步PQ预测算法，该算法随着选择的输入PQ参数的数量逐渐增加，开发了AI模型。在每个下一步中，将形成一个更复杂的模型，使用附加的相关PQ输入以更高的精度计算其目标PQ输出。具有逐步增长的PQ输入的PQ模型，使用在上一步中预测的数据，可以更好地估计和估计目标数量。