For optimal utilization of power generation resources, load forecasting plays a vital role in balancing the load flow in a power distribution network. There are several drawbacks associated with existing forecasting techniques for load flow balancing. Neural network (NN) based forecasting techniques are unable to consider the actual states of a power system, while weighted least squares state estimation (WLS) fails to counter nonlinearity in the demand profile. In this article, a hybrid approach is proposed for short term load forecasting. The hybrid technique, comprised of a WLS, NN, and adaptive neuro-fuzzy inference system (ANFIS), is termed WLANFIS. ANFIS itself is the combination of an NN and fuzzy logic. It takes a refined data set obtained through NN and WLS, which helps in determining the optimal number and types of membership functions. It also helps in determining the effective fuzzy set ranges for an individual membership function that is used by the fuzzy system. WLS provides estimated states in the real-world scenario while the NN models the nonlinearity in the demand profile and is tested on IEEE 14 and 30 bus systems as well on real-world data sets. Results show that the proposed algorithm has a higher generalization capability and provides accurate forecasting results even in the case of medium-term load forecasting. It outperforms other methodologies by achieving a mean absolute percentage error as low as 2.66%.

中文翻译：

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智能电网中通过估计参数化模糊推理系统进行负荷预测


为了优化利用发电资源，负荷预测在平衡配电网负荷流方面发挥着至关重要的作用。现有的潮流平衡预测技术存在一些缺点。基于神经网络 (NN) 的预测技术无法考虑电力系统的实际状态，而加权最小二乘状态估计 (WLS) 无法应对需求曲线中的非线性。在本文中，提出了一种用于短期负荷预测的混合方法。这种混合技术由 WLS、NN 和自适应神经模糊推理系统 (ANFIS) 组成，称为 WLANFIS。 ANFIS 本身是神经网络和模糊逻辑的结合。它需要通过 NN 和 WLS 获得的精炼数据集，这有助于确定隶属函数的最佳数量和类型。它还有助于确定模糊系统使用的单个隶属函数的有效模糊集范围。 WLS 提供真实场景中的估计状态，而 NN 对需求曲线中的非线性进行建模，并在 IEEE 14 和 30 总线系统以及真实数据集上进行测试。结果表明，该算法具有较高的泛化能力，即使在中期负荷预测的情况下也能提供准确的预测结果。它的平均绝对百分比误差低至 2.66%，优于其他方法。