## 3 Results and Discussion  
Figure 6 shows two examples of the community power load forecast from the start time to 40 min into the future. The blue plot line is the power prediction output of the ANN that used the LSTM's predictions of the future weather conditions. The red plot line is the power prediction output of the ANN that used the measured (actual) weather conditions, which illustrates the ability of the ANN to forecast the power load having the perfect weather measurements but no knowledge of the past weather conditions. The error between the measured power load readings (green) and the predictions  
Figure 5: Illustration of the multilayer perceptron artificial neural network implementation for the power draw predictions. Each neuron uses a rectified linear unit (ReLU) as the activation function. The example network shows six inputs, two hidden layers, and one output. (blue) are much smaller then the error between the measured power load (green) and the ANN's discrete power load predictions using the perfect weather data. The power predictions that used the LSTM's future weather forecast resulted in the overall smaller error which is an evidence that the temporal model generalized the weather trends in the past weather window to produce higher accurate power load predictions. In contrast, the ANN makes power load predictions with greater error even if it has the perfect weather measurements which do not take into the account the past weather conditions. In other words, the ANN error to predict the power load was consumed by the LSTM memory of the past weather which produced a smoothing effect for the ANN predictions. The overall prediction trends in Figure 6 illustrate the instability of the power load predictions made further out into the future. The instability in the left sample is visible as a non-monotonic prediction trend and a high variance of the prediction 30 min into the future. The sample prediction output on the right shows the instability by the divergence of the predictions from the measured power load, especially noticeable for the predictions 30 and 40 min into the future. The cumulative assessment of the predictions using the validation set is presented as a correlation plot between the actual power load measurements and the outputs of the ANN that used the LSTM weather predictions to predict the power load. Figure 7 shows the future predictions on the x-axis and the measured power load on the y-axis. The points closest to the diagonal are near perfect predictions, while the points below the diagonal are higher predictions than measured and the lower predictions for the measured power load are above the diagonal. Figure 6: Two sample output of the power load predictions. The blue plot line shows the predicted power load by the ANN that used the LSTM’s outputs of the predicted weather conditions, the red plot line is the ANN’s power load prediction using the measured weather conditions, and the green solid plot line is the actual measured power load. Each major unit on the x-axis is a prediction of 10 min into the future from the start time. Figure 7 (left) shows the current power load predictions using current weather conditions without any knowledge of the past weather. The results are reported after 200 training epochs on the validation data. The plot does not show any future predictions, it shows how well the ANN generalized the association of the weather conditions and the power load. Each point is a prediction of a current power load given the measured weather conditions. Model's overall error is to produce lower power load predictions in comparison to the measured behavior which is seen as the predictions' trend-line slope greater than 45\({}^{\circ}\). Figure 7 (right) shows the future power load predictions using the future weather conditions predicted by the LSTM's outputs. Again, the ideal power load predictions are the prediction points on the plot's diagonal.