Deep learning approach to forecasting hourly solar irradiance

Abstract: In this dissertation, six artificial intelligence (AI) based methods for forecasting solar irradiance are presented. Solar energy is a clean renewable energy source (RES) which is free and abundant in nature. But despite the environmental impacts of fossil energy, global dependence on it is yet to drop appreciably in favor of solar energy for power generation purposes. Although the latest improvements on the technologies of photovoltaic (PV) cells have led to a significant drop in the cost of solar panels, solar power is still unattractive to some consumers due to its unpredictability. Consequently, accurate prediction of solar irradiance for stable solar power production continues to be a critical need both in the field of physical simulations or artificial intelligence. The performance of various methods in use for prediction of solar irradiance depends on the diversity of dataset, time step, experimental setup, performance evaluators, and forecasting horizon. In this study, historical meteorological data for the city of Johannesburg were used as training data for the solar irradiance forecast. Data collected for this work spanned from 1984 to 2019. Only ten years (2009 to 2018) of data was used. Tools used are Jupyter notebook and Computer with Nvidia GPU...M.Ing. (Electrical and Electronic Engineering Management

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1 degrees C and 2 degrees C, respectively

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

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Feature Papers of Forecasting

Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented

Data driven tools to assess the location of photovoltaic facilities in urban areas

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.This work has been supported by the project RTI2018-095097-B-I00 at the 2018 call for I+D+i Project of the Ministerio de Ciencia, Innovación y Universidades, Spain. Funding for open access charge: Universidad de Málaga/CBUA, Spain

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids. With unparalleled data granularity, a data-driven system could better anticipate solar generation. Deep learning (DL) models have gained popularity due to their capacity to handle complex datasets and increase computing power. This article introduces the Galactic Swarm Optimization with Deep Belief Network (GSODBN-PPGF) model. The GSODBN-PPGF model predicts PV power production. The GSODBN-PPGF model normalises data using data scaling. DBN is used to forecast PV power output. The GSO algorithm boosts the DBN model’s predicted output. GSODBN-PPGF projected 0.002 after40 h but observed 0.063. The GSODBN-PPGF model validation is compared to existing approaches. Simulations showed that the GSODBN-PPGF model outperformed recent techniques. It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day