PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

  title={PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context},
  author={S. Preda and S. Oprea and Adela B{\^a}ra and Anda Belciu},
Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much” energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to… Expand
Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania
A methodology that automatically collects the data logs from sensors installed on PV arrays, inverters and weather stations, checks the health status of the PV components, forecasts the generated power for each inverter based on its real operating conditions and the predicted irradiance and finally provides useful insights of the solar energy system based on the Key Performance Indicators (KPI) using big data technologies is proposed. Expand
Data analysis of a monitored building using machine learning and optimization of integrated photovoltaic panel, battery and electric vehicles in a Central European climatic condition
The study shows that machine learning can be used for the investigation of the monitored data, which can be later used for simulating and optimizing the case studies of the photovoltaic-based energy system integrated with onsite battery and electric vehicles for a demo site in Belgium. Expand
Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting
Evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features demonstrated that the optimally constructed BNN outperformed all other machinelearning models achieving forecasting accuracies lower than 5%. Expand
A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania
A Smart Adaptive Switching Module (SASM) architecture, which efficiently uses electricity generation of local available RES by gradually switching electric appliances based on weather sensors, power forecast, storage system constraints and other parameters, is proposed. Expand
Title: Method of monitoring and detection of failures in PV system based on machine learning techniques
Machine learning methods have been used to solve complicated practical problems in different areas and are becoming increasingly popular today. The purpose of this article is to evaluate theExpand
Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm
A novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output to show that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-S SA and LSTM-Ssa methods. Expand
A survey of big data and machine learning
Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Expand
An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation
An inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. Expand
Distributed Systematic Grid-Connected Inverter Using IGBT Junction Temperature Predictive Control Method: An Optimization Approach
The prediction model of the improved chicken swarm optimization-support vector machine is proposed to forecast the IGBT module junction temperature and has a positive impact on improving the distributed systematic grid-connected inverter industrial development and promotes the new energy usage. Expand
Smart city big data analytics: An advanced review
A systematic review of the literature on smart city big data analytics, a technological and thematic analysis of the shortlisted literature, and a classification model that studies four aspects of research in this domain are presented. Expand


Big Data Analytics for Dynamic Energy Management in Smart Grids
The big data issues and challenges faced by the DEM employed in SG networks are highlighted, a brief description of the most commonly used data processing methods in the literature is provided, and a promising direction for future research in the field is proposed. Expand
Forecasting Regional Photovoltaic Power Generation - A Comparison of Strategies to Obtain One-Day-Ahead Data
Abstract There are several ways to obtain forecasts of photovoltaic, PV, power depending on the required accuracy, forecast horizon, climate and other conditions. In this study, we evaluate 3Expand
Big Data management in smart grid: concepts, requirements and implementation
An overview of data management for smart grids is provided, the added value of Big Data technologies for this kind of data is summarized, and the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context are discussed. Expand
The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid
Recommendations and practices to be used in the future of smart grid and Internet of things are provided and the different applications of smart sensor networks in the domain of smart power grid are explored. Expand
Solar Energy Forecasting and Resource Assessment
Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energyExpand
Big data driven smart energy management: From big data to big insights
Large amounts of data are increasingly accumulated in the energy sector with the continuous application of sensors, wireless transmission, network communication, and cloud computing technologies. ToExpand
Remote Monitoring System Using WSN for Solar Power Panels
Remote monitoring system for a set of Photo Voltaic (PV) panels, using a wireless sensor network WNS is our contribution in this work, we present a simulation interface for PV Panels. This researchExpand
Development of a Wireless Sensor Network for Individual Monitoring of Panels in a Photovoltaic Plant
The system proposed succeeds in identifying all the nodes in the network and provides real-time monitoring while tracking efficiency, features, failures and weaknesses from a single cell up to the whole infrastructure, which contributes to reducing failures, wastes and, consequently, costs. Expand
Apache Spark a Big Data Analytics Platform for Smart Grid
Abstract Smart grid is a complete automation system, where large pool of sensors is embedded in the existing power grids system for controlling and monitoring it by utilizing modern informationExpand
The objectives of this study are (a) development of optimized regression and ANN (artificial neural network) models for EEC (electrical energy consumption) forecasting based on several optimizationExpand