Distribution fault outage cost evaluation and hotspot area recognition based on big data

Chen, Jin‐Ming; Yang, Yi; Wang, Ziying; Yan, Li; Guo, Youming; Cui, Jinli; Zhu, Daohua

doi:10.1049/joe.2017.0435

Cited by 1 publication

(1 citation statement)

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“…Similarly, big data related to SG is being utilized by researchers to increase the efficiency, reliability, and stability of the grid along with making it environmentally friendly. In this regard, researchers have proposed forecasting models to predict the future demand [ 1 ], price [ 2 , 3 , 4 ], energy generation [ 5 ], and fault and anomalies of power systems [ 6 ] using big data. The implementation of these models can effectively increase the throughput of the modern grid.…”

Section: Introductionmentioning

confidence: 99%

Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

Khalid

Javaid

Al-Zahrani

et al. 2019

Entropy

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In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

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