Datasets

Data used for the training surrogate modeli and further results preparation as figures presented in the paper: Sauhats, A.; Zalostiba, D.; Petrichenko, R.; Bockarjova, G.; Burcevs, K.; Junghans, G.; Eisons, E. Power Systems Transition Simulation Using Artificial Neural Networks and Surrogate Modelling. https://doi.org/10.3390/en19153607 In more detail: energies_Dataset(in).csv contains 130,000 simulations of the average of two-week electricity price in the Latvian bidding zone. The simulations were performed using a detailed mathematical model (Matlab software) of the united Baltic power system. The input parameters used for the training process are: Total electricity demand of the united Baltic power system over a two-week period, MWh Electricity generation from solar power plants over a two-week period, MWh Electricity generation from wind farms over a two-week period, MWh Installed capacity of a small modular nuclear reactor (SMR), MW Operating cost of reserve power capacity, EUR/MWh Transfer capacity of the Lithuania-Poland interconnection, MW Electricity price in Poland, EUR/MWh Transfer capacity of the Estonia-Finland interconnection, MW Electricity price in Finland, EUR/MWh Transfer capacity of the Lithuania-Sweden interconnection, MW Electricity price in Sweden, EUR/MWh The target variable for the training process is the 12th parameter, namely the average two-week electricity price in the Latvian bidding zone. During the detailed model simulations, parameters 1 through 11 were varied independently and randomly within predefined ranges. As a result of these simulations, a dataset containing 130,000 observations of the average two-week electricity price in the Latvian bidding zone was generated. The simulations described above serve as the training data for the surrogate model presented in the article. In energies_DataSet_for_3d_cgar(in).csv three parameters used to construct the 3D plot. The data represent a random sample of 30,000 observations for the following three parameters:1. Installed solar generation capacity in the united Baltic power system, MW2. Installed wind generation capacity in the united Baltic power system, MW3. Transfer capacity of the Lithuania–Poland interconnection, MW