Frontiers in Energy Research · 2023 · 13 citations · 20 references
ComputingComputer ScienceData ScienceForecastingMachine LearningRenewable Energy SystemsDeep LearningDistributed Energy ResourcesRenewable EnergySolar Photovoltaic SystemsSpatial PredictionTime Series PredictionAccuracyDeep Learning For ForecastingDeep Learning For Time Series ForecastingDeep Learning-based ForecastingPhotovoltaic Power ForecastingSolar Power ForecastingSpatio-temporal DependenceSpatio-temporal Forecasting
To obtain higher accuracy of PV prediction to enhance PV power generation technology. This paper proposes a spatio-temporal prediction method based on a deep learning neural network model. Firstly, spatio-temporal correlation analysis is performed for 17 PV sites. Secondly, we compare CNN-LSTM with a single CNN or LSTM model trained on the same dataset. From the evaluation indexes such as loss map, regression map, RMSE, and MAE, the CNN-LSTM model that considers the strong correlation of spatio-temporal correlation among the 17 sites has better performance. The results show that our method has higher prediction accuracy.
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Predicting residential energy consumption using CNN-LSTM neural networks
Tae Young Kim, Sung-Bae Cho · Energy · 2019 · 1.4K citations