## 1 Introduction
the morning and evening hours, or lower power consumption during the business hours, and does not have valleys during the commute periods. In addition to the ability to predict the community power load for the microgrids operating at locations with extreme weather conditions, we also address the question if the power load prediction using multiple weather factors will outperform the prediction accuracy using only the temperature measurements. Although outside the scope of this project, the ability to accurately predict power generation from renewable energy sources in Northern latitudes is equally important and uncertain. The power generator manufacturers test their devices for a limited range of operating conditions which might not include extreme operating conditions that commonly occur in Alaska. As a result, the expected power curves often do not hold, so building machine learning models that associate the ambient environmental conditions with the power generation are needed. For example, the photovoltaic systems will produce electricity that exceeds the manufacturer's maximum power generation simply because the photovoltaic cells installed in the Arctic are super cooled [6]. The energy production using the in-river hydroturbines is effected by sediment transport [7]. The icing on the wind-turbine blades creates added drag [8]. The concentration of the reflected ambient light from snow is often sufficient to produce electricity even during the low light conditions when no power generation is expected. The Long Short Term Memory (LSTM) is a type of recurrent artificial neural network (RNN) model where the network's inputs are processed in stages and the output of each stage (layer) is not only fed as the input to the next state, but the outputs are also reused as the inputs to the previous processing layers [9; 10]. Additionally, the conventional node in a RNN is converted to a module that contains three gates. Each gate is a neural network with specific memory tasks, those being forget, input, and output. This architectural design creates directed loops in the information processing and captures the temporal dependency between consecutive instances of input data and thus implements the system's memory. The architecture was first introduced in 1997 by [PERSON] et al. [9] and since the LSTMs were used in the electrical power domain to disaggregate the power signature of a single dominant appliance or a sub-circuit from the aggregate power signal [2], predict the wind power generation from the environmental conditions [3], and detect anomaly in sensor networks [11; 12]. [PERSON] et al. used the LSTM to predict wind power generation [3]. They used the principal component analysis (PCA) to narrow down the number of input variables from the original five variables that are included in the wind turbine output power equations to only two: wind velocity and wind direction. The cumulative contribution of these two parameters was 99.8% of the overall data variance and the resulting LSTM predicted the wind power generation with an accuracy of 1.07% normalized root mean squared error. The long short term memory model for load forecasting with empirical mode decomposition was proposed by [PERSON] et al. [4]. The model forecasts the next hour peak energy load in the power grid as well as one day ahead load forecasting. The LSTM was trained on the grouping of days with similar environmental characteristics. The groupings were determined using K-means clustering algorithms with extreme gradient boosting [13]. The resulting prediction model had \((0.93,1.13)\) training error and \((1.01,1.34)\) mean absolute percentage error for the next day peak load prediction depending on the model's configuration. [PERSON] et al. designed a hybrid short term load forecast model to predict the power demand from load time series using neural networks and genetic algorithms-based models [14].