# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 4 Results
\begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Parameters Considered** \\ \hline AR & Autogressive prediction for each hour is based on the same hour in the past 14 days. Coefficients are presented in Table 2 \\ \hline MLP & Multi-Layer Perceptron. Input matrix [24,11]. Input variable: Past 14 days, Day of week, Working/non-working day and hours, special days, temperature. 2 \(\times\) hidden layers (300, 200). Output layer: Dense; Activation: Relu; Optimizer: [PERSON]; Loss: MSE; Epochs: 100 \\ \hline Simple RNN & Recurrent neural network. Input matrix [24,11]. Input variable : Past 14 days, Day of week, Working/non-working day and hours, Special days, temperature, humidity, dew point. 3 \(\times\) hidden layers (100, 100, 96). Output layer: Dense; Activation: Tanh, Sigmoid; Optimizer: [PERSON]; Loss: MSE; Epochs: 100 \\ \hline LSTM & Long short-term memory. Input matrix [24,11]. Input variable: Past 14 days, Day of week, Working/non-working day and hours, special days, temperature, humidity, dew point. 3 \(\times\) hidden layers (100, 100, 168). Output layer: Dense; Activation: Tanh, Sigmoid; Optimizer: [PERSON]; Loss: mean square error; Epochs: 100 \\ \hline LSTM & Input matrix [24,11]. Input variable: Past 14 days, Day of week, Working/non-working day and hours, special days, temperature, humidity, dew point. 3 \(\times\) hidden layers (100, 100, 48). Output layer: Dense; Activation: Tanh, Sigmoid; Optimizer: [PERSON]; Loss: mean square error; Epochs: 100 \\ \hline GRU & Gated recurrent unit. Input matrix [24,11]. Input variable: Past 14 days, Day of the week, Working/non-working day and hours, special days, temperature, humidity, dew point. 3 \(\times\) hidden layers (100, 100, 48). Output layer: Dense; Activation: Tanh, Sigmoid; Optimizer: [PERSON]; Loss: mean square error; Epochs: 100 \\ \hline GRU\(-\)LSTM & Combination of LSTM and GRU layers. Input matrix [24,11]. Input variable: Past 14 days, AR(9), Day of week, Working/non-working day and hours, special days, temperature, humidity, dew point. 3 \(\times\) hidden layers (100, 100, 48). Output: Dense; Activation: Tanh, Sigmoid; Optimizer: [PERSON]; Loss: MSE; Epochs: 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters used by the forecast methods implemented in this work. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **AR(9)** & **LSTM enc dec** & **LSTM-GRU** & **LSTM** & **MLP** & **Simple RNN** & **GRU** \\ \hline MAPE & 5.53\% & 6.28\% & 5.14\% & 5.43\% & 5.71\% & 6.63\% & 4.82\% \\ \hline RMSE & 0.146 & 0.193 & 0.138 & 0.141 & 0.165 & 0.181 & 0.131 \\ \hline MAE & 0.112 & 0.145 & 0.1001 & 0.104 & 0.129 & 0.140 & 0.0998 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forecast errors for the test dataset: 15 october-20 december. the AR component it helps when the behaviour of the consumer is repetitive and improves the forecast. Other ML algorithms have good results, but none score better than the GRU. In Figure 7 high variations can be observed in the peak hours. Simple RNN scored the highest MAPE 6.63%, even worse than the autoregressive model. The LSTM encoder-decoder is a sequence to the sequence learning algorithm, performed worse than the LSTM by 14.89% because more trainable weights for each of the encoder's time steps are required. The consequence of this situation is a large number of parameters if the input data for the encoder is a long time series, even more parameters than the LSTM. A complex network needs a longer time for training, and the underfitting problem can cause the LSTM encoder-decoder to perform worse than the LSTM. Because we implemented a similar structure for all the RNN networks to offer a solid comparison benchmark, we can conclude that the LSTM encoder-decoder could improve forecasting results if the complex network would be trained for a longer period with a more powerful hardware resource. In article [46], the authors compare LSTM with GRU on text datasets, and conclude that through empirical research, the advantage of GRU is relevant in the scenario of small datasets.