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

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions