## 3 Building Energy Consumption Forecasting
### Forecasting Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Author** & **Contribution** & **Limitation** \\ \hline
[69] & [PERSON] et al. & A methodology for a day-ahead photovoltaic power generation probabilistic prediction for building energy management system applications. & The methodology was tested in a specific climate and location. \\ \hline
[70] & [PERSON] et al. & A method to forecast thermal and electricity demand in a nonresidential building. & The method excluded the building’s physical parameters. \\ \hline
[71] & [PERSON] et al. & To utilize the models that support the area of cooling load demand forecasting for short-term and medium-term. & As the number of samples increased, the accuracy of the method decreased due to particular aspects of the load curve \\ \hline
[72] & [PERSON] et al. & An approach to estimate diurnal cooling load energy utilization for nonresidential building & The occupancy information for the building was not included in the data analysis. \\ \hline
[73] & [PERSON] et al. & A dynamic prediction model for building cooling loads that consolidate an ensemble method with an ANN. & The utilization of calendar information to demonstrate the occupancy situation. \\ \hline
[74] & [PERSON] et al. & A load consumption forecasting method for nonresidential buildings using artificial occupancy features. & Imprecision in determining human behavior. \\ \hline
[75] & [PERSON] et al. & An ANN-based approach to forecast cooling and heating load in residential buildings. & Just a single climatic zone was included in the development of the method. \\ \hline
[76] & [PERSON] et al. & A method to estimate energy utilization supported by alternative information sources, for example, the number of vehicles in a parking area. & The method for energy forecasting was based on the fact that the occupancy prediction model was accurate. \\ \hline
[77] & [PERSON] et al. & A forecasting model to characterize and estimate the energy utilization of residential and nonresidential buildings using machine learning. & The method created was restricted to residential and nonresidential buildings from a topographical zone. \\ \hline
[78] & [PERSON] et al. & A methodology to forecast peak load demand in nonresidential buildings using ANN with external variables. & The methodology did not consider the occupancy in the forecast of peak load demand. \\ \hline
[79] & [PERSON] et al. & A methodology to estimate the building heating and cooling load utilization to discover the peak load of a water source heat pump in the early and operation stage. & This methodology applies just to buildings and service companies that have sufficient monitoring information, and is proper for building without retrofitting in the computing time frame. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._The hybrid methods need approaches to recognize and mirror the timetable and occupants' intensity change, plug loads, and lighting [82]. For existing buildings with accessible monitoring information, the hybrid method is considered to consolidate the best of two universes: physical understanding and model structure, and boundary estimation and measurable framework [83]. Using a hybrid method frequently requires long computation time because of the boundary advancement procedure and master information during the model improvement process [84]. There have been studies on hybrid methods that have focused on electrical consumption [85; 86; 87; 88; 89; 90; 91; 92], cooling and heating load [93; 94; 95; 96; 97; 98; 99; 100; 96], energy consumption [96; 97; 98; 101], thermal load [102], thermal response [103] and load demand [104]. Based on the aforementioned studies, a summary of their contributions and limitations is presented in Table 3.