Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones

Uncertainty-Aware Workload Prediction in Cloud Computing

Predicting future resource demand in Cloud Computing is essential for managing Cloud data centres and guaranteeing customers a minimum Quality of Service (QoS) level. Modelling the uncertainty of future demand improves the quality of the prediction and reduces the waste due to overallocation. In this paper, we propose univariate and bivariate Bayesian deep learning models to predict the distribution of future resource demand and its uncertainty. We design different training scenarios to train these models, where each procedure is a different combination of pretraining and fine-tuning steps on multiple datasets configurations. We also compare the bivariate model to its univariate counterpart training with one or more datasets to investigate how different components affect the accuracy of the prediction and impact the QoS. Finally, we investigate whether our models have transfer learning capabilities. Extensive experiments show that pretraining with multiple datasets boosts performances while fine-tuning does not. Our models generalise well on related but unseen time series, proving transfer learning capabilities. Runtime performance analysis shows that the models are deployable in real-world applications. For this study, we preprocessed twelve datasets from real-world traces in a consistent and detailed way and made them available to facilitate the research in this field

Generation and Evaluation of Space-Time Trajectories of Photovoltaic Power

In the probabilistic energy forecasting literature, emphasis is mainly placed on deriving marginal predictive densities for which each random variable is dealt with individually. Such marginals description is sufficient for power systems related operational problems if and only if optimal decisions are to be made for each lead-time and each location independently of each other. However, many of these operational processes are temporally and spatially coupled, while uncertainty in photovoltaic (PV) generation is strongly dependent in time and in space. This issue is addressed here by analysing and capturing spatio-temporal dependencies in PV generation. Multivariate predictive distributions are modelled and space-time trajectories describing the potential evolution of forecast errors through successive lead-times and locations are generated. Discrimination ability of the relevant scoring rules on performance assessment of space-time trajectories of PV generation is also studied. Finally, the advantage of taking into account space-time correlations over probabilistic and point forecasts is investigated. The empirical investigation is based on the solar PV dataset of the Global Energy Forecasting Competition (GEFCom) 2014.Comment: 33 pages, 11 Figure