This thesis aims to develop a framework for evaluating the quality of large-scale forecasts in the energy sector. The research of this thesis intends to identify the factors that influence the forecasting process and to develop metrics to evaluate the quality of the forecast. Although various metrics exist for evaluating forecast quality, there has yet to be a consensus on which metrics should be used for evaluating large-scale forecasts. To address this problem, a case study was conducted on the BelVis+Forecast software, which is widely used for energy forecasting. The case study was based on semi-guided interviews with the stakeholders involved in the forecasting process. The Goal-Question-Metric (GQM) approach was used to identify the key points of interest and develop metrics for evaluating the quality of the forecast. The main findings of this thesis are the six major areas of influence on the forecasting process: process performance, data quality, data and forecast quantity, forecast quality, process status, and data extrapolation. Various aspects of each topic were analysed, and multiple metrics were generated to evaluate the quality of the forecast. The framework that was developed can be used to analyse the complete forecasting process of the BelVis+Forecast software. This can help improve the accuracy and reliability of energy forecasts, which is essential for the efficient operation of energy systems.