Electricity price forecasting plays a fundamental role in ensuring efficient market operation and informed decision making. With the growing integration of renewable energy, prices have become more volatile and difficult to predict, increasing the necessity of accurate forecasting in bidding, scheduling, and risk management. This paper provides a comprehensive review of point forecasting models for electricity markets, covering classical statistical approaches both with and without exogenous inputs, and modern machine learning and deep learning techniques, including ensemble methods and hybrid architectures. Unlike standard reviews focused solely on the day-ahead market, we assess model performance across day-ahead, intra-day, and balancing markets, with each posing unique challenges due to differences in time resolution, data availability, and market structure. Through this market-specific lens, the paper merges insights from a broad set of studies; identifies persistent challenges, such as data quality, model interpretability, and generalisability; and outlines promising directions for future research. Our findings highlight the strong performance of hybrid and ensemble models in the day-ahead market, the dominance of recurrent neural networks in the intra-day market, and the relative effectiveness of simpler statistical models such as LEAR in the balancing market, where volatility and data sparsity remain critical challenges.

Accurately forecasting workload demand in cloud computing environments is essential for optimizing resource allocation, minimizing costs, and ensuring reliable service quality. As cloud computing scales to meet the needs of diverse applications - from AI and machine learning to data-intensive analytics - predictive models play a critical role in dynamically managing multiple resources. Traditional models provide limited guidance for decision-makers since they are typically univariate models, ignoring the prediction of the interplay between multiple resources, and do not account for the uncertainty of their predictions, preventing resource management from acting promptly according to such uncertainty to ensure specific target service level requirements. To address these limitations, we introduce univariate and bivariate Bayesian deep learning models that predict future workload demand of one and multiple resources respectively, while quantifying the uncertainty of their predictions. In particular, our approach leverages Hybrid Bayesian Neural Networks and probabilistic Long Short-Term Memory models, enhanced with architecture modifications to handle complex, multivariate cloud workload patterns. Moreover, we investigate fine-tuning-based transfer learning methods to enhance their adaptability in real-world cloud scenarios where new data centres with different workload characteristics operate. We validate our models on extensive datasets from Google and Alibaba cloud clusters. Results show that modelling the uncertainty of predictions positively impacts performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications. Moreover, transfer learning benefits performance in scenarios where models are built on data from the same provider.

To compare different forecasting methods on demand series, we require an error measure. Many error measures have been proposed, but when demand is intermittent some become inapplicable because of infinities, some give counter-intuitive results, and there is no agreement on which is best. We argue that almost all known measures rank forecasters incorrectly on intermittent demand series. We propose several new error measures with almost no infinities, and with correct forecaster ranking on several intermittent demand patterns. We call these ‘mean-based’ error measures because they evaluate forecasts against the (possibly time-dependent) mean of the underlying stochastic process instead of point demands.