Dalcali Adem, Özbay Harun, Duman Serhat
Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Bandirma Turkey.
Department of Electrical Engineering, Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Bandirma Turkey.
Concurr Comput. 2022 Jul 10;34(15):e6947. doi: 10.1002/cpe.6947. Epub 2022 Mar 22.
The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression-feedforward artificial neural network (MLR-FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID-19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR-FFANN approach was to simultaneously optimize the polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR-FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID-19 precautions.
能源消耗的增加受到技术发展以及全球人口增长的影响。能源消耗的增加使得确保电力供应安全变得困难。通过合理规划可以满足能源需求。合理规划对于资源的经济利用和终端用户的低成本至关重要。另一方面，需求估计错误可能导致资源浪费和能源危机。通过准确地对影响电力消耗的因素进行建模，可以实现准确估计。除了季节条件、一周中的天数和小时数等已知因素外，对影响我们所有行为的大流行等极端事件进行建模，会提高未来预测建模的成功率。这确保了在资源有限的情况下有效地保障电力供应安全。为此，在本研究中，考虑到新冠疫情对土耳其工业城市布尔萨能源消耗值的估计影响，提出了一种基于混合多元线性回归 - 前馈人工神经网络（MLR - FFANN）的算法模型。混合MLR - FFANN方法的目的是使用自适应引导差分进化、平衡优化器、黏菌算法和带有适应度距离平衡的随机分形搜索（SFSFDB）优化算法，同时优化多元线性回归的多项式以及前馈传播神经网络的权重和偏差系数。根据平均绝对误差、平均绝对百分比误差和均方根误差评估标准以及这些结果的统计分析，确定了使用优化算法优化参数后的模型的成功率。根据分析结果，用SFSFDB算法优化参数的MLR - FFANN方法在包含新冠疫情预防措施的数据集训练中更成功。
