In this article, the impact of prediction errors on the performance of a domestic power demand management is thoroughly investigated. Initially, real-time peak power demand management system using battery energy storage systems (BESSs), electric vehicles (EVs), and photovoltaics (PV) systems is designed and modeled. The model uses real-time load demand of consumers and their roof-top PV power generation capability, and the charging–discharging constraints of BESSs and EVs to provide a coordinated response for peak power demand management. Afterward, this real-time power demand management system is modeled using autoregressive moving average and artificial neural networks-based prediction techniques. The predicted values are used to provide a day-ahead peak power demand management decision. However, any significant error in the prediction process results in an incorrect energy sharing by the energy management system. In this research, two different customers connected to a real-power distribution network with realistic load pattern and uncertainty are used to investigate the impact of this prediction error on the efficacy of an energy management system. The study shows that in some cases the prediction error can be more than 300%. The average capacity of energy support due to this prediction error can go up to 0.9 kWh, which increases battery charging–discharging cycles, hence reducing battery life and increasing energy cost. It also investigates a possible relationship between environmental conditions (solar insolation, temperature, and humidity) and consumers’ power demand. Considering the weather conditions, a day-ahead uncertainty detection technique is proposed for providing an improved power demand management.

中文翻译：

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预测误差对家庭峰值用电需求管理的影响

在本文中，彻底研究了预测误差对家用电力需求管理性能的影响。最初，设计和建模使用电池储能系统（BESS），电动汽车（EV）和光伏（PV）系统的实时峰值功率需求管理系统。该模型利用了消费者的实时负载需求及其屋顶光伏发电能力，以及BESS和EV的充放电约束条件，为高峰用电需求管理提供了协调响应。然后，使用自回归移动平均和基于人工神经网络的预测技术对实时电力需求管理系统进行建模。预测值用于提供提前一天的峰值功率需求管理决策。然而，预测过程中的任何重大错误都会导致能源管理系统分配不正确的能源。在这项研究中，使用具有实际负载模式和不确定性的连接到实际配电网络的两个不同客户来研究此预测误差对能源管理系统功效的影响。研究表明，在某些情况下，预测误差可能超过300％。由于该预测误差，能量支持的平均容量可以达到0.9 kWh，这会增加电池的充放电周期，从而缩短电池寿命并增加能源成本。它还研究了环境条件（日照，温度和湿度）与消费者的电力需求之间的可能关系。考虑到天气情况，