## 1 Introduction  
The rural communities of Alaska are often self-managed, electrical microgrid islands that primarily rely on diesel generators for power production. In recent years, an increasing number of rural communities have started to diversify their energy sources and integrate renewable energy sources into their microgrids. Many communities in Southeast Alaska rely on hydropower generation, the Southcentral communities use solar and wind power, while the subarctic communities of the North include geothermal, wind and in-river hydrokinetic power to offset the fossil fuel-based power generation (Note: Juneau, AK is not a microgrid and 100% of the city's power demand is hydropower generated). The integration of the renewable sources of energy into microgrids is economically advantageous, but also presents risks and uncertainty. The frequency of extreme weather events is increasing which makes the long term predictions of the renewable power generation and the community's power demand a challenge. In 2008, an unexpected increase in temperature and precipitation resulted in an avalanche that disrupted the water supply to the hydropower generators and resulted in the disruption of the electrical feed into Juneau. The event lasted six and a half weeks [1]. The isolation of many communities also adds to power insecurity as repairs and diesel fuel resupplies are often delayed due to bad weather. The research goal is to build a forecast model that will predict the community power load for microgrid installations in highly variable weather environments, with the intent to integrate the model into the next generation electrical microgrid management system (EMMS). Existing research addresses the prediction of a community power load or a renewable power generation for the microgrid installations at locations with stable weather conditions in comparison to the common extreme weather changes of many Alaskan communities (Figure 1). Our results show that the power load prediction using a previously proposed, monolithic, recurrent neural network-based model would yield future power load predictions with only 10% accuracy [2; 3; 4]. Limited research exists on how to model the electrical microgrid behavior for environments where the ambient temperature changes exceed 10 \({}^{\circ}\)F in less than 20 min, multiple times a day, with sustained wind speeds of 60 mph and the wind gusts exceeding 100 mph possibly lasting several days, and sudden changes of the solar radiation dropping the power output from photovoltaic installations in excess of 80% [5]. The novel contributions of this research are: (1) the design of a multistage, hybrid machine learning architecture that decouples the temporal predictions of a single model into two sub-models, the first one for the temporal predictions for future weather conditions and the second to associate the predicted future weather conditions with the expected power load and (2) the prediction of power load solely from the past weather observations without the models using the past power load data as their inputs. Figure 1 shows the power load data is missing in mid-March and mid-November. The advantage of using a model ensemble that does not to rely on the past power load readings as their input is to make power predictions even for the days without the reported power load readings by using only the past weather conditions as the model inputs. The results show that the combined predictive accuracy of two individual sub-models is relatively low, but as an ensemble the framework will have high power load prediction accuracy. Finally, the proposed architecture illustrates the use of small data (less than one year) to successfully train and validate the individual sub-model. Figure 2 shows the design details of proposed hybrid machine learning architecture that consists of two sub-models. The advantage of dividing the prediction into two sub-models successfully mitigates the high volatility of the weather conditions and the associated uncertainty of predicting the community power load.