Data Mining Models for Short Term Solar Radiation Prediction and Forecast-Based Assessment of Photovoltaic Facilities

Solar radiation prediction is useful to integrate photovoltaic power plants into the electrical system. Integrating energy generation in urban environments is interesting because that is where the most energy is consumed and avoids wasting energy in transport infrastructure. Renewable energies are often the easiest to integrate into these environments because they require less infrastructure and cause fewer problems related to noise, dirt, pollution, etc. The overall objective of this thesis is to develop data mining models to forecast solar global radiation 24 hours ahead and to use these predictions to evaluate the performance of photovoltaic systems. The specific objectives are: 1. Propose an index that allows us to remove the seasonal and daily trends observed in global hourly radiation data. 2. Analyze the different sources of meteorological variables that can be used to predict solar radiation and use API's to access external sources of meteorological data. 3. Develop data mining models that allow including the different relationships observed between the radiation values of the next day depending on the values of the current day radiation and other meteorological parameters. 4. Development of a web system that include the proposed models for short-term radiation forescasting and integrate the developed models in the evaluation models of photovoltaic systems. Chapter 3 introduces the methods and models used in this work (Cumulative Probability Distribution Function, Artificial Neural Networks and Support Vector Machines). Also classification methods are presented (Decision Trees and Support Vector Machines for Classification). Performance metrics are presented to measure the accuracy of the proposed models. The data sets and data sources used in this work to test the proposed models are presented, including data from the meteorological station installed at University of Malaga, data from OpenWeatherMap website and data from AEMET (Agencia Estatal de Meteorología). Chapter 4 is dedicated to the solar radiation fundamentals, including astronomical concepts related to Earth-Sun position, characterization of solar radiation hourly series, clearnes index, used to remove seasonal trends, persistence model, used to compare with proposed models and the forecast skill, based on persistence model and used as reference model as well. Chapter 5 introduces a model to model and characterize hourly solar global radiation using statistical methods like CPDF, K-means, and also using the clearness index. This models aims to predict the hourly solar radiation using the daily clearness index as input. Chapter 6 details the proposed model to forecast the hourly global solar radiation using data mining methods and daily profiles of clearness index. K-means is again used to cluster daily solar radiation profiles, then a new variable is defined from the clearness index daily profiles. Support Vector Machines, Decision Trees and Artificial Neural Networks are used to predict the desired hourly solar radiation values. Chapter 7 presents a methodology to assess solar power plants performance based on forecasted solar radiation. A OPC-based system is presented, which is able to obtain data from a large variety of equipment, then an algorithm to assess the performance of the plants is presented

Analyzing big time series data in solar engineering using features and PCA

In solar engineering, we encounter big time series data such as the satellite-derived irradiance data and string-level measurements from a utility-scale photovoltaic (PV) system. While storing and hosting big data are certainly possible using today’s data storage technology, it is challenging to effectively and efficiently visualize and analyze the data. We consider a data analytics algorithm to mitigate some of these challenges in this work. The algorithm computes a set of generic and/or application-specific features to characterize the time series, and subsequently uses principal component analysis to project these features onto a two-dimensional space. As each time series can be represented by features, it can be treated as a single data point in the feature space, allowing many operations to become more amenable. Three applications are discussed within the overall framework, namely (1) the PV system type identification, (2) monitoring network design, and (3) anomalous string detection. The proposed framework can be easily translated to many other solar engineer applications

Cost benefit analysis and data analytics for renewable energy and electrical energy storage

To accommodate with the global increase in the deployment of solar photovoltaic (PV) and energy storage system (ESS), a deterministic approach for sizing PV and ESS with anaerobic digestion biogas power plant; to meet a load demand will be presented in this plenary session. This aim is to maximize the sizing of PV to increase the security of energy supply. Energy economics for ESS will be a focus. Case study based on real-life data will be used to demonstrate the validity of the new approach

Daily clearness index profiles and weather conditions studies for photovoltaic systems

© 2017 The Authors. The increasing number of distributed photovoltaic (PV) systems connected to the power grid has made system planning and performance evaluation a challenging task. This is mainly due to the computational complexity, such as load flow analysis with large irradiance datasets collected from various locations of the installed PV farms. Solar irradiance data are known to possess the characteristic of high uncertainty, due to the random nature of cloud cover and atmospheric conditions. This paper presents the studies on the relationships of clustered clearness index profiles and the weather conditions obtained from the weather forecasting stations. Four years of solar irradiance and weather conditions data from two locations (Johannesburg and Kenya) were obtained and are used for the analysis. The preliminary study shows that the weather condition is related to the daily clearness index profiles. This work will form the basis for estimating the daily clearness index profile with weather conditions.Department of Finance and Education of Guangdong Province 2016[202]: Key Discipline Construction Programme and Education Department of Guangdong Province: New and integrated energy system theory and technology research group [project number 2016KCXTD022]

Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods

Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited. This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data. All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data

Optimal structure of a Smart DC micro-grid for a cluster of zero net energy buildings

© 2016 IEEE.A decarbonized society involves people living and working in low-energy and low-emission buildings. A smart multi-Terminal DC micro-grids interconnecting several autonomous zero-net energy buildings (ZNEBs) allow the transition to a decarbonized economy, however, involves several challenges. This paper evaluates the problem of an optimal topology for a cluster of several ZNEBs and it takes several advantages related to the holistic operation and planning. A high-resolution electricity demand model is used together with several scenarios of stochasticity (weather, human behaviour, etc.) in order to create several credible scenarios of electricity demand at each ZNEB and then solve the constrained optimization problem of two network topologies for the cluster of interacting ZNEB. This paper has demonstrated the appropriate performance of the proposed approach using a verys simple, demonstrative/illustrative, example, a cluster of three DC-Houses

A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing

Many distribution system studies require long-term evaluations (e.g. for one year or more): Energy loss minimization, reliability assessment, or optimal rating of distributed energy resources should be based on long-term simulations of the distribution system. This paper summarizes the work carried out by the authors to perform long-term studies of large distribution systems using an OpenDSS-MATLAB environment and parallel computing. The paper details the tools, models, and procedures used by the authors in optimal allocation of distributed resources, reliability assessment of distribution systems with and without distributed generation, optimal rating of energy storage systems, or impact analysis of the solid state transformer. Since in most cases, the developed procedures were implemented for application in a multicore installation, a summary of capabilities required for parallel computing applications is also included. The approaches chosen for carrying out those studies used the traditional Monte Carlo method, clustering techniques or genetic algorithms. Custom-made models for application with OpenDSS were required in some studies: A summary of the characteristics of those models and their implementation are also included.Peer ReviewedPostprint (published version

Cost benefit analysis and data analytics for renewable energy and electrical energy storage

EPSRC (Engineering and Physical Sciences Research Council) EP/P022049/