Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks.

中文翻译：

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用于确定性和概率低压负载预测的混合集成深度学习

准确可靠的低压负荷预测对于配电网和智能电网的优化运行和控制至关重要。然而，与传统的高电压区域负荷预测相比，由于低容量负荷和分布式可再生能源并入需求侧固有的高度不确定性，它面临着严峻的挑战。本文提出了一种用于确定性和概率性低压负载预测的新型混合集成深度学习 (HEDL) 方法。深度置信网络（DBN）应用于低压负载点预测，具有很强的非线性映射逼近能力。引入了包括bagging和boosting变体在内的一系列集成学习方法来提高DBN的回归能力。此外，应用bagging和boosting方法利用差分变换技术保证负载时间序列的平稳性。在集成学习的集成思想的基础上，通过集成多种单独的集成方法，开发了一种新的混合集成算法。考虑到各种集成算法的多样性，利用有效的K最近邻分类方法自适应地确定子模型的权重。此外，通过利用bagging和boosting中固有的重采样思想，提出了基于HEDL的概率预测。HEDL 方法在确定性和概率性预测中的有效性已基于华东和澳大利亚的实际负荷数据得到系统验证，