# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 2 Literature Review  
A plethora of approaches consisting of time series analysis, regression, smoothing techniques, artificial intelligence, artificial neural networks, machine learning, deep learning, reinforcement learning and various hybrid methods can make this area of research overwhelming. Some authors suggest that established models are better [7; 8; 9] presents evidence that complexity harms accuracy. Authors in [10] propose the Golden Rule to provide a unifying theory of forecasting, while others embed multiple algorithms to build hybrid methods combining characteristics of traditional statistics and machine learning. There is truth on both sides; some algorithms will work better or worse depending on historical data or applied period. The forecasting objectives are to minimize errors and improve economic activity: revenue, profit, and higher customer satisfaction. Low error forecasts are of no inherent value if ignored by the industry or otherwise not used to improve organizational performance. Forecasting competitions presented in [11; 12] is one of the best ways to compare algorithms on reliable historical data and point out \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Market Entity** & **Advantages** & **Disadvantages/Obstacles** \\ \hline \multirow{3}{*}{DSO} & optimized power flow; RES integration; lower grid losses & big data; high and inconsistent errors by similar models and methods (depending on the period of time, load category, forecasting window); additional resources required (time, qualified employees, software, training) \\ \hline \multirow{3}{*}{TSO} & lower transmission losses, better planning & big data processing; high errors; inconsistent forecasting; \\ \hline Energy Supply & better trade, lower costs for portfolio balancing & client commitment; communication about activity planning; high errors or inconsistency of load forecasting; \\ \hline End-user & lower bills, increased energy efficiency & low interest, as main activities are more important than electricity usage; lack of transparency; \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Impact of load forecasting. Figure 1: Classification of time series forecasting techniques. the results. In multiple cases, recurrent neural networks (RNN) architecture stands out as a stable algorithm. The work done by [13] presents an extensive experimental study using seven popular DL architectures and found that LSTM is the most robust type of recurrent network, and while LSTM provides the best forecasting accuracy, convolutional neural networks (CNN) are more efficient and suffer less variability of results.In this paper, various RNN networks are applied for industrial load forecasting and analyzed to establish the best architecture for deep recurrent neural networks. In the article [14] authors point out that the difference between simple RNN to GRU and LSTM is that the number of parameters increases, a conclusion also presented in our article. For 24 h ahead forecasting commercial building data, authors concluded that the DNN model achieved worse results than the sequence to sequence RNN models. The authors in [15] present a simple recurrent neural network for the one-hour prediction of residential electric load. The model takes as inputs weather data as well as data related to electricity consumption. The percentage error calculated for a week test is 1.5% for the mean error and 4.6% for the maximum error. The difference between industrial load and residential usage is that the latter is highly dependent on weather data and daily patterns are more repetitive. In our article, exogenous variables such as temperature, humidity, and dew point are used in forecasting, because the industrial processes analyzed are influenced by these variables.