Power Generation Forecasting

Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs (Input dataset)

The dataset represents the input data on which the article Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs, is based. The data consist of a two-year hourly time series of measured wind speed and direction, air density, and production of two wind farms (WTs) in Croatia (Bruška and Jelinak). In addition to the two listed WTs, measurements of two nearby WTs (Glunca and Zelengrad) are also attached in training files (these WPPs are not directly analyzed in the article).

Categories:: Machine Learning
Power and Energy

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Short-term Photovoltaic Power Forecasting based on Long Short Term Memory Neural Network and Attention Mechanism

Photovoltaic (PV) power generation forecasting is an important topic in field of power system, energy conversion and smart grid construction. The PV power generation has the properties of randomness and volatility is due to the variability of solar irradiance, temperature and other meteorological factors. In order to reduce the volatility, accurate PV power generation forecasting techniques are demanded.

Categories:: Power and Energy

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