# Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
## 3 Results
### AI Applications in Energy Demand Forecasting and Optimization for Emission Reduction  
In addition to directly affecting energy generation, another way to use AI for emission reduction in the energy sector is to forecast the future. A clear prediction of the future can be used to set policies and better plan the actions based on the prediction. When planned correctly, the policies and actions can reduce emissions from the energy sector. Forecasting the future can be beneficial on multiple levels. The following section includes studies that used forecasting at national, power grid, and end-user levels to reduce emissions. [PERSON] et al. [56] tested Linear Regression, Support Vector Regression, and Random Forest Regression algorithms to forecast the electricity demand at a regional level in Guandong Province, China. The ML algorithms used economic data and weather data as explanatory variables, in addition to electricity consumption and production from neighboring provinces and the electricity production of the Guandong province. The authors suggested that by forecasting the future electricity demand, the power generation firms can better plan the required electricity production in advance, therefore producing the electricity in more optimized manner, thus potentially lowering the greenhouse gas emissions. [PERSON] et al. [57] studied the prediction of power plant redispatch events that were caused by transmission congestion. During the congestion events, the power plants in front of the congestion need to ramp down their production (negative redispatch), and the plants behind the congestion need to ramp up their production (positive redispatch). The authors tested multiple different ML methods for redispatch event prediction and discovered that artificial neural network (ANN) and extra-tree classifier performed the best. The authors trained separate models for negative and positive redispatch types. The model output was the probability that the power plant is redispatched during the following day. The authors reported the average F\({}_{1}\)-scores of 0.47 for the ANN and 0.42 for the extra tree; however, the score varied significantly between the different power plants. The authors argued that the proposed method can be used to prepare for plant redispatch events, and the transmission system operator can therefore identify power plants that can be completely ramped down for the duration of the congestion instead of ramping down the production of multiple plants by small amounts. This can reduce emissions since the other power plants can continue running with optimal power output. [PERSON] et al. [58] proposed additive artificial neural networks (AANNs) for energy consumption estimation and prediction in residential buildings. In addition to AANN models, the authors also tested other AI models, such as two flavors of support vector regression (SVR) models and a classical artificial neural network. The dataset was sourced from residential buildings that used solar photovoltaic system as a renewable energy source. The dataset had a one-hour resolution (sampling time). The predictive accuracy of the different AI methods was compared with Mean Absolute Percentage Error, Mean Absolute Error, Root Mean Square Error and Correlation Coefficient. The AANN was identified as the best performing forecasting model to predict buildings' energy consumption. The authors claimed that building energy consumption prediction is the basis for building performance optimization and therefore building energy reduction. Energy usage reduction leads to less energy used; thus, it can lead to emission reductions. [PERSON] et al. [59] investigated whether an ML model is capable of finding the least-cost and robust pathways to achieve a net-zero electricity system in Canada by 2050. The model input variables included estimates such as carbon tax, demand growth and annualized capital costs for different energy sources like wind, solar, coal, and nuclear energy.