Solar Power Forecasting

Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately predict solar power. We first explored the potential of instance-based methods and proposed two new methods: the data source weighted nearest neighbour (DWkNN) and the extended Pattern Sequence Forecasting (PSF) algorithms. DWkNN uses multiple data sources and considers their importance by learning the best weights based on previous data. PSF1 and PSF2 extended the standard PSF algorithm deal with data from multiple related time series. Then, we proposed two clustering-based methods for PV power prediction: direct and pair patterns. We used clustering to partition the days into groups with similar weather characteristics and then created a separate PV power prediction model for each group. The direct clustering groups the days based on their weather profiles, while the pair patterns consider the weather type transition between two consecutive days. We also investigated ensemble methods and proposed static and dynamic ensembles of neural networks. We first proposed three strategies for creating static ensembles based on random example and feature sampling, as well as four strategies for creating dynamic ensembles by adaptively updating the weights of the ensemble members based on past performance. We then explored the use of meta-learning to further improve the performance of the dynamic ensembles. The methods proposed in this thesis can be used by PV plant and electricity market operators for decision making, improving the utilisation of the generated PV power, planning maintenance and also facilitating the large-scale integration of PV power in the electricity grid

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or win

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

Production planning of energy systems: Cost and risk assessment for district heating

This dissertation is a collection of research articles that assess economic andoperational risk in production planning of district heating. District heatingsystems are typically coupled to the electricity system through cogenerationand power-to-heat technologies, and production planners must account foruncertainty stemming from changing weather, demands and prices. Years ofhigh-resolution data from the district heating system in Aarhus, Denmark havebeen used throughout the project to model the system and estimate uncertainties.Risk management tools have been developed to aid district heating operatorsand investment decision makers in short-, medium- and long-term productionplanning.Short-term production planning involves commitment of production unitsand trading on the electricity markets and relies on forecasts of the heat load.Weather predictions are a significant source of uncertainty for heat load forecasts,because the heat load is highly weather-dependent. I introduce the method ofensemble weather predictions from meteorology to heat load forecasting andcreate a probabilistic load forecast to estimate the weather-based uncertainty.Better estimates of the weather-based uncertainty can be applied to optimizesupply temperature control and reduce heat losses without compromising securityof supply in heat distribution systems.Consumer behavior is another substantial, but difficult to capture, source ofuncertainty in short-term heat load forecasts. I include local holiday data instate-of-the-art load forecasts to improve accuracy and capture how load patternschange depending on the behavior of the consumers. A small overall improvementin forecast accuracy is observed. The improvement is more significant on holidaysand special occasions that are difficult to forecast accurately.In medium-term production planning, there can be substantial economicpotential in performing summer shutdown of certain production units. Theshutdown decision carries significant risk, due to changing seasonal weatherpatterns. Based on 38 years of weather data, the uncertainty on the timing ofthe optimal decision is estimated. This information is used to develop practicaldecision rules that are robust to rare weather events and capable of realizingmore than 90% of the potential savings from summer shutdown.Long-term production planning decisions regarding investments in futuredistrict heating production systems are affected by uncertainty from changingelectricity prices, fuel prices and investment cost for technology. The effects ofthese uncertainties on a cost-optimal heat production system are explored, usingwell-established production and storage technologies and extensive multivariatesensitivity analysis. The optimal technology choices are highly stable and,taxes aside, large heat pumps and heat storages dominate the cost-optimal heatproduction systems. However, the uncertainty on the exact capacity allocationis substantial. Excluding heat production based on fossil fuels increases theuncertainty on the system cost, but drastically reduces the uncertainty on theoptimal capacity allocation