# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 5 Gaussian Process Usage in Terms of Electrical Energy
### Electricity Usage and Generating Prediction  
Nowadays, more and more devices and structures use electricity, so the prediction of its usage can be an important matter especially in terms of smart cars or smart homes where it can be both a matter of proper operation and of huge savings. The same principles guide the authors of [29], where the main aim is to predict energy consumption by different kinds of buildings which can lead to optimizing it through special control and low-energy strategies. General workflow starts from collecting and preparing data, which comes from actual buildings. Being more accurate, from six different commercial buildings and its energy use database. The principal parameters were chosen by taking into account their impacts on the building electricity use. Those impacts were measured by calculating the correlation between energy use and meteorological parameters. The authors also considered users' behavior. Then the whole set was filtered, cleansed and parsed in order to be used to predict the electricity usage [29]. GP regression is used as the prediction algorithm. The authors claim that, compared to the popular support vector machine (SVM) method, GPR shows great advantages in learning the kernel and regularization parameters, integrated feature selection, and generating fully probabilistic predictions. The model treats the occupancy schedule and the meteorological parameters as inputs. The output is building energy consumption. GP models were trained on a training dataset, and the model gives a numeric value of energy use of the test dataset based on the known inputs. The whole framework is shown in Figure 3[29]. Data used in the creation and validation of model came from shopping malls, offices and hotels. The statistical analysis of results were conducted by authors. They calculated Root Mean Squared Error (RMSE) as well as Normalized Mean Bias Error (NMBE) and R-square to catch the accuracy of the model's prediction. Conclusions were that GPR was regarded as a very accurate way of predicting the energy usage in this case study. It also generates predictions in very limited CPU time (at around 0.02 s per prediction). GPR can catch different energy patterns and can be used with very different inputs such as weather conditions, occupancy activities and others. Moreover, the authors claim that GPR can also be used with problems connected to predictions of the thermal dynamics of buildings and variable operations. In addition it could be compiled with a control system based on deviation of prediction. On the other hand, GP can also be used for power generation prediction. In [30] we can see the comparison of some methods used for the prediction of the wind turbine power curve (WTPC). WTPC is a characteristic of the turbine which shows the relationship between output power and wind speed. Stochastic and intermittence exist in the generated wind power, which affect the safety of the power grid. The prediction of wind power is also very critical in wind power dispatching and even in enhancing wind turbine controllability [30]. Methods chosen by authors to predict WTPC are as follows:  
* Gaussian Bin method;
* kernel density estimation (KDE);
* conditional kernel density estimation (CKDE);
* conditional probability via Copula;
* relevance vector machine (RVM);
* and of course Gaussian Process Regression. In this review, we will focus only on GPR usage and its results in this problem. The whole process of setting up GP lacks a detailed description. Hyperparameters of covariance functions are obtained by maximizing the marginal likelihood. The authors also notice that GPR is adopted for WTPC modeling, so both point estimation and confidence intervals can be calculated. Interval modeling was based on a brief description in [31]. The main issue with GPR is computational complexity, which is unfeasible for large datasets. Results differ amongst all methods and depend on what was to be accomplished.