There have been numerous simulation tools utilised for calculating building energy loads for efficient design and retrofitting. However, these tools entail a great deal of computational cost and prior knowledge to work with. Machine Learning (ML) techniques can contribute to bridging this gap by taking advantage of existing historical data for forecasting new samples and lead to informed decisions. This study investigated the accuracy of most popular ML models in the prediction of buildings heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data generated in EnergyPlus and Ecotect and compared the results. The study used a grid-search coupled with cross-validation method to examine the combinations of model parameters. Furthermore, sensitivity analysis techniques were used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated. Comparing the accuracy of the tuned models with the original research works reveals the significant role of model optimisation. The outcomes of the sensitivity analysis are demonstrated as relative importance which resulted in the identification of unimportant variables and faster model fitting.

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Many municipalities and public authorities have supported the creation of solar cadastres to map the solar energy potential of existing buildings. Despite advancements in modelling solar potential, most of these tools provide simple evaluations based on benchmarks, neglecting the effect of uncertain environmental conditions and that of the spatial aggregation of multiple buildings. We argue that including such information in the evaluation process can lead to more robust planning decisions and a fairer allocation of public subsidies. To this end, this paper presents a novel method to incorporate uncertainty in the evaluation of the solar electricity generation potential of existing buildings using a multi-scale approach. It also presents a technique to visualize the results through their integration in a 3D-mapping environment and the use of false-colour overlays at different scales. Using multiple simulation scenarios, the method is able to provide information about confidence intervals of summary statistics of production due to

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