Ghadimi Noradin, Yasoubi Elnazossadat, Akbari Ehsan, Sabzalian Mohammad Hosein, Alkhazaleh Hamzah Ali, Ghadamyari Mojtaba
Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran.
College of Technical Engineering, The Islamic University, Najaf, Iraq.
Heliyon. 2023 Jun 3;9(6):e16827. doi: 10.1016/j.heliyon.2023.e16827. eCollection 2023 Jun.
With the introduction of various loads and dispersed production units to the system in recent years, the significance of precise forecasting for short, long, and medium loads have already been recognized. It is important to analyze the power system's performance in real-time and the appropriate response to changes in the electric load to make the best use of energy systems. Electric load forecasting for a long period in the time domain enables energy producers to increase grid stability, reduce equipment failures and production unit outages, and guarantee the dependability of electricity output. In this study, SqueezeNet is first used to obtain the required power demand forecast at the user end. The structure of the SqueezeNet is then enhanced using a customized version of the Sewing Training-Based Optimizer. A comparison between the results of the suggested method and those of some other published techniques is then implemented after the method has been applied to a typical case study with three different types of demands-short, long, and medium-term. A time window has been set up to collect the objective and input data from the customer at intervals of 20 min, allowing for highly effective neural network training. The results showed that the proposed method with 0.48, 0.49, and 0.53 MSE for Forecasting the short-term, medium-term, and long-term electricity provided the best results with the highest accuracy. The outcomes show that employing the suggested technique is a viable option for energy consumption forecasting.
近年来，随着各种负荷和分散生产单元接入系统，短期、长期和中期负荷精确预测的重要性已得到认可。实时分析电力系统性能并对电力负荷变化做出适当响应，以充分利用能源系统，这一点很重要。在时域内进行长期电力负荷预测，可使能源生产者提高电网稳定性、减少设备故障和生产单元停电，并保证电力输出的可靠性。在本研究中，首先使用SqueezeNet来获取用户端所需的电力需求预测。然后使用基于缝纫训练的优化器的定制版本来增强SqueezeNet的结构。在将该方法应用于具有短期、长期和中期三种不同需求类型的典型案例研究后，将该方法的结果与其他一些已发表技术的结果进行了比较。设置了一个时间窗口，每隔20分钟从客户处收集目标数据和输入数据，以实现高效的神经网络训练。结果表明，所提出的方法在预测短期、中期和长期电力时的均方误差分别为0.48、0.49和0.53，具有最高的准确率，提供了最佳结果。结果表明，采用所提出的技术是进行能源消耗预测的一个可行选择。
