Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems

Ding, Ni; Benoît, Clémentine; Foggia, Guillaume; Bésanger, Yvon; Würtz, Fréderic

doi:10.1109/tpwrs.2015.2390132

Cited by 182 publications

(83 citation statements)

References 41 publications

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“…Previous work has shown that classic feedforward ANN may provide outstanding results in time series prediction tasks [7,[10][11][12][13][14]24,25]. In this study, we have selected two neural network architectures aimed specifically at this problem to be used as prediction models for energy consumption.…”

Section: Methodsmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

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Section: Methodsmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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“…We can conclude that GRU neural networks do better in both convergence speed and training time, which depends on the improved single structure of GRU units. We also performed the experiments to compare with current methods such as back-propagation neural networks (BPNNs) [7,8], stacked autoencoders (SAEs) [17], RNNs [24,25], and LSTM [29][30][31]. Their parameters and structures are set as described in Section 3.2.…”

Section: Comparison Of Results Of Proposed Methodsmentioning

confidence: 99%

Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

et al. 2018

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Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial intelligent methods have been applied to short-term load forecasting in past research, but most did not consider electricity use characteristics, efficiency, and more influential factors. In this paper, a method for short-term load forecasting with multi-source data using gated recurrent unit neural networks is proposed. The load data of customers are preprocessed by clustering to reduce the interference of electricity use characteristics. The environmental factors including date, weather and temperature are quantified to extend the input of the whole network so that multi-source information is considered. Gated recurrent unit neural networks are used for extracting temporal features with simpler architecture and less convergence time in the hidden layers. The detailed results of the real-world experiments are shown by the forecasting curve and mean absolute percentage error to prove the availability and superiority of the proposed method compared to the current forecasting methods.

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“…Besides, the response time of load demand to temperature changes is larger than that of the sampling period. Therefore the average temperature of the past 3 h (Tav(3)), 6 h (Tav(6)), and 24 h (Tav (24)) are selected as temperature variables [40]. (1) Historical load.…”

Section: Feature Set Constructionmentioning

confidence: 99%

A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction

Cai

Wang

2016

Energies

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Abstract:In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches.

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Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems

Cited by 182 publications

References 41 publications

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks

A Novel Hybrid Short Term Load Forecasting Model Considering the Error of Numerical Weather Prediction

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