# Deep Learning for Short-Term Load Forecasting--Industrial Consumer Case Study
## 3 Materials and Methods  
The forecasting methods are implemented in this paper use hourly data (Figure 2) from an industrial company active in the wood processing industry for an entire year (2019). The power supply for the factory is provided through twelve power transformers summing 12.6 MVA. The following technological processes, machinery, and equipment determine the electricity consumption forecasted in this article:  
* Installations that serve the equipment for cutting and exhaust;
* Installations that serve the cooling system to ensure the necessary cold to keep in optimal conditions the substances used in the foaming process;
* Installations that serve the processing and cutting of sponges;
* The installations that serve the different subsections when making the mattresses;
* Equipment used for making upholstery and assembling all subassemblies;
* Interior lighting installations located in the physical perimeter of all production halls;
* Robots for packing finished products;Conveyors for the transport of products in the logistics warehouse;
* Specific facilities for food preparation in the canteen;
* other installations are specific to the universal processes which take place within this undertaking.  
For the implementation Tensorflow [20] was used for deep learning applications. Keras [21] is a high-level API, open-source library for machine learning that works on top of Tensorflow. For the data preparation and visualization of the results, Scikit-learn [22], Numpy [23], and Seaborn [24] were used. The simulations computed on a PC Intel(R) Core(TM) i5-4690K [EMAIL_ADDRESS] GHz, RAM 16 GB, 64-bit operating system, x64-based processor.  
The industrial consumer analyzed is a furniture factory consisting of all the technological processes necessary to manufacture furniture starting from raw wood, mainly electric drives. The consumer energy needs are electricity and wood scraps. Production of heat and hot water relies on burning the remaining wood from the technological processes. The heating in the winter period for the office building and factory production facilities is achieved with electric heaters which influence the consumption in the winter period together with the lightning systems (work schedule is in three shifts). High electricity consumption is driven by large ventilated storage halls used for the thermal preparation of the raw wood. Correlation between electric load and outdoor temperature, dew point, and humidity is observed. Working/non-working days load patterns are not the same because factory planning is highly dependent on production quota. A Dickey-Fuller test [25] made for the yearly load time series points to the null hypothesis and the non-stationarity of the time series. Reliable linear dependencies between exogenous variable and consumption could not be establish and deep learning became an option to explore for nonlinear dependencies. From all the algorithms implemented in this article, variations of RNN (LSTM, GRU, GRU-LSTM), the GRU algorithm offered the best result for forecasting. Given this reason, we tried to analyze which is the best structure for the GRU for our particular problem.