Vivas Eliana, Allende-Cid Héctor, Salas Rodrigo
Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Brasil 2950, Valparaíso, Chile.
Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Chacabuco 2092-2220, Valparaíso, Chile.
Entropy (Basel). 2020 Dec 15;22(12):1412. doi: 10.3390/e22121412.
Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.
电力预测在当前电力系统的管理和平衡中起着重要作用。因此，需要准确预测服务需求，以便为电力的生产和分配制定更好的规划，并降低电力系统集成中出现漏洞的风险。为了本研究的目的，我们进行了系统的文献综述，以确定在电力预测方面最有可能表现出高精度的模型类型。根据来自五个地理区域的257次准确性测试报告的结果，确定了精确电力预测方面的最新模型。我们比较了两类预测模型：经典统计或数学（MSC）模型和机器学习（ML）模型。此外，我们还确定了对电力预测做出重大贡献的混合模型的使用情况，并通过一个案例研究来证明其与传统模型相比的良好性能。在我们的主要发现中，我们得出结论，通过缩短时间范围可以将预测误差降至最低，考虑各种外生变异性来源的机器学习模型往往具有更好的预测准确性，最后，在过去五年中，预测模型的准确性有了显著提高。
