Short-term solar energy forecasting: Integrated computational intelligence of LSTMs and GRU.

ABSTRACT: Problems with erroneous forecasts of electricity production from solar farms create serious operational, technological, and financial challenges to both Solar farm owners and electricity companies. Accurate prediction results are necessary for efficient spinning reserve planning as well as regulating inertia and power supply during contingency events. In this work, the impact of several climatic conditions on solar electricity generation in Amherst. Furthermore, three machine learning models using Lasso Regression, ridge Regression, ElasticNet regression, and Support Vector Regression, as well as deep learning models for time series analysis include long short-term memory, bidirectional LSTM, and gated recurrent unit along with their variants for estimating solar energy generation for ever

SUBMITTER: Zameer A 

PROVIDER: S-EPMC10550187 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature