## 1 Introduction
The first temporal sub-model is trained to predict the near-future weather conditions given the past weather data (Figure 2 LSTM) while the second sub-model is trained to associate the historical weather conditions with the community power demand (Figure 2 ANN). The sub-models are validated individually on how accurately they predict the weather conditions and the power load respectively with reference to the recorded data. Finally, the power load prediction uses the past weather conditions as the inputs to the first temporal sub-model to produce the future predictions of weather conditions as its output. The outputs of the first sub-model are used as the inputs to the second sub-model to produce the power load predictions. The model ensemble accuracy is measured as a difference between the final predicted power load outputs from the second sub-model against the measured power load values. Since the first sub-model outputs the future weather conditions, thus making predictions in time, the second sub-model does not use time and only associates the future predicted weather with the power load. The advantage of dividing the prediction into two sub-models include (1) the increase in accuracy in comparison to a single model with small data and (2) the ability to reuse the second sub-model to predict behavior of different electrical components of an EMMS. For example, Figure 2 shows the wind power model as an alternative reuse of the second sub-model to associate the predicted weather conditions with the wind power generation. The wind power generation predictions would use the same outputs of the first temporal sub-model but associate them with the wind power outputs in the second sub-model. In this case, the second sub-model is trained to associate the weather conditions with the wind power output. This design allows for an easy integration of any number of alternative energy generation sources or power load measured at different sub-stations as the EMMS prediction modules--where each EMMS module would correspond to one trained sub-model. This reuse of the second sub-model would not be possible if the model ensemble architecture would rely on the power readings as its inputs. In the future, we plan to use the community power load and the renewable power generation models to optimize and control the fossil fuel-based power generators (either diesel or coal fired  
Figure 1: **Top**: Annual weather conditions at the Igiugig, AK study site. **Bottom**: Not an uncommon pattern of weather changes in one day. Notice several sudden temperature changes in access of 10 \({}^{\circ}\)F occurring over 20 min window. generators). The overall \"super-model\" architecture showed in Figure 2 can be used to implement the electrical microgrid management system which would allow for the long term planning of the fuel supplies, integration and optimization of a battery bank, and the intelligent scheduling of the community's cyber-enabled, energy load dependent infrastructure to distribute the microgrid's power load. One example is scheduling the operation of a micro water-treatment facility outside of peak demand times or when the EMMS generates excess power. Figure 1 shows the measured power load behavior has low correlation to any one of the weather factors. Also, the power load does not have an easily discernible (asympto  
Figure 2: An illustration of the power prediction super-model. The weather conditions, power load and power generation data are showed in the Temporal Data table. The solid arrows indicate the use of data for the model training, the double arrows show the prediction of power load or power generation. While the illustration shows the overall energy microgrid management system (EMMS), we present only the details of the Long Short Term Memory and the Power Load Artificial Neural Networks models. The EMMS simply integrates the power load prediction and the power generation from the renewable sources.