Search

The insulation paper loss of life (LOL) of a distribution transformer (DT) is largely determined by its winding hot-spot temperature. It is mainly estimated via the transformer load, ambient temperature, and related physical parameters. Fortunately, advanced metering infrastructure (AMI) can provide load profiles of DTs, allowing a cost-effective LOL estimation solution. However, the AMI dataset contains measurement errors that significantly reduce LOL estimation accuracy. A forecasting method is needed to recover the erroneous AMI data. This is a challenging problem, as the load profiles of DTs are nonstationary and volatile. We propose an ensemble of stacked long short-term memory (ES-LSTM) networks to simultaneously capture load consumption patterns of DTs and their correlations. The ES-LSTM consists of two independent LSTM networks and a feed-forward neural network (FFNN) stacked on top of them. A two-stage training procedure is applied. In the first stage, two LSTM networks are trained to capture the daily or weekly load patterns. In the second stage, the FFNN is trained to optimally combine the last hidden outputs of the two LSTM networks. Field verification is conducted using real AMI data obtained from an urban distribution utility in China. The test results confirmed the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) increases the complexity of forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from the data set. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to convert the continuity feature of input data into binomial distribution feature. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on the genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]