Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most.

This paper provides a focus upon forecasting electricity prices in the medium term (from a few weeks to several months ahead) in which accurate estimates of tail risks, e.g., at the 1%, 5%, 95%, and 99%, are important. Medium term forecasting and risk analysis are important for operational scheduling, fuel purchasing, trading, and profit management. We extend the research on hybrid forecasting methods, which link detailed fundamental price formation models, using optimization techniques and market equilibrium considerations, with econometric recalibration to the time series data. This paper is innovative in its use of quantile regression to undertake the recalibration and provide accurate risk estimates. It is shown that probabilistic outputs from the fundamental model add value over expected value inputs to the quantile regressions and that if the fundamental model is itself well specified to diurnal variation through the inclusion of relevant explanatory variables such as demand or climatic conditions, then it is not necessary to undertake the quantile regressions separately for each hour of the day. A real application of the proposed methodology is successfully tested on the Spanish electric power system, in which the high penetration of intermittent wind generation creates extreme price risks. The hybrid method outperforms the more conventional fundamental model, making particular use of wind generation data in the quantile recalibrations.