Deep Neural Network based on Genetic Algorithm and Ensemble Methods for Regional Solar Power Forecasting in Thailand This article presents Deep Neural Network (DNN) based on Genetic Algorithms (GA) and Ensemble Methods for Regional Solar Power Forecasting in Thailand for one day in advance by using forecast weather data, real measured data and Solar power data and applying DNN with structure (window size, number of neurons in hidden layer, activation function, loss function, optimizer, numbers of epoch) designed with GA as well as using Ensemble Methods to blend the forecast values from each model together with Neural Network to improve predictive accuracy which is the solar power forecast. This article uses a total of 7 power plants with total power generation capacity of 436 MW. The strength of the proposed method is 1) Using inputs from multiple models to improve accuracy. 2) Weights are provided for weather forecast station surrounding power plants. 3) Models are structurally designed with GA. 4) Combining the advantages and decreasing the disadvantages of other models, by applying all these techniques for forecasting the total power generation capacity of all 7 solar power plants as a case study and found that the Root Mean Square Error was 5.61%, which decreased by 1.71% compared to the single model that provides the best forecasting results.

Shortterm Electricity Load Forecasting Using a MapReducebased Elman Networks with Coarsegrained Parallel Genetic Optimization How to make full use of highdimensional massive power data to improve the accuracy and efficiency of shortterm electrical load forecasting is a challenging problem to be solved. This paper discusses a parallel load forecasting technique with an Elman Neural Network(ENN) based on the MapReduce(MR) programming model. Specially, a CoarseGrained Parallel Genetic Algorithm(CPGA) is introduced into the process of ENN training for obtaining the promising weights and thresholds. In the framework of MR, a parallel loadforecasting model of MR CPGAENN is established. Case study with different scenario based electrical load data sets and meteorological information confirm that both the accuracy and efficiency of the developed model are superior to the conventional models compared.