## 4 Conclusions  
We presented a hybrid machine learning approach to predict the community power load dynamics. Although the scope of presented work is to model one aspect of the energy management system, the hybrid super-model can be expanded to contain the power generation models and the diesel generator control system. Each of the renewable models would generalize the behavior of a single renewable energy source such as a photovoltaic, hydrodynamic, or wind-turbine power generators. Modeling behavior of the renewable energy sources would use the same modeling approach as the community power load: the LSTM would predict the future weather conditions and the behavior of each renewable energy source would be modeled by an individual ANN. Each ANN would bind predicted future weather conditions and the power production of a renewable energy source. The advantage of designing multiple models to predict the electrical microgrid behavior is in the transparency of the resulting electrical microgrid management systems. With dedicated ANN models, each constituent component of the control system can be individually examined, optimized or  
Figure 9: The sample outputs of the LSTM models for the events, wind direction, wind gust speed, conditions, temperature, dew point, wind speed and temperature weather features (from the top left to the bottom right in a column-row order). The green plot line is the measured (actual) value of the reported weather feature and the red plot line is the LSTM output predicting the weather conditions for the current and 40 min into the future. controlled. The prediction mechanism is divided into the temporal weather condition LSTM model and the ANN power model which allows for the reuse of the LSTM by multiple power models. In other words, if new weather features or additional weather readings become available, only the LSTM model has to be retrained to increase the prediction accuracy of the future weather conditions, while the power models can remain unchanged. We illustrated how a relatively small data set can be generalized using hybrid model design that breaks up the prediction task into two submodels. Unlike the previous research, the proposed machine learning algorithm does not group the similar weather days into clusters to train the model, instead we define past and future weather windows for the current condition readings and use this data as a discrete data point to train the LSTM. Treating each reading as a discrete data point allows the algorithm to predict future weather conditions that include dynamic and extreme weather events. Using a single LSTM that would associate the current weather conditions with the community power load forecast yields low accuracy of less than 10%. The main reason for low accuracy is the fact that the model was expected to perform a two step inference of predicting what the weather would be in the near future and associating the weather prediction with the power load. By splitting a single LSTM model into two submodels we minimized the prediction error in each submodel. We hypothesize that a single LSTM model might achieve high prediction accuracy using a sufficiently large data set to train a single LSTM. The available data for the presented work had less than one year of power load readings with a limited number of unique weather events (discretized and binned). For example, the data set contains only one weather event of extremely low temperature below \(-35\)\({}^{\circ}\)F. To improve the model accuracy, the training and the validation sets have to include multiple events in each weather regime category (conditions bin). Using a small data set also limited which additional input features were used to construct the model. For example, the temporal LSTM does not use the day of the week or the time of the day, because the model training and validation required multiple instances of similar weather vectors. Given the available data, many of the weather regime bins had a low number of weather vectors.