Artificial Intelligence for Solar Energy Harvesting in Wireless Sensor Networks

Solar cells have been extensively investigated for wireless sensor networks (WSN). In comparison to other energy harvesting techniques, solar cells are capable of harnessing the highest amount of power density. Furthermore, the energy conversion process does not involve any moving parts and does not require any intermediate energy conversion steps. Their main drawback is the inconsistent amount of energy harvested due to the intermittency and variability of the incoming solar radiation [1]. Consequently, being able to predict the amount of solar radiation is important for making necessary decisions regarding the amount of energy that can be utilized at the sensor node. We demonstrate that artificial intelligence (AI) can be used as an effective technique for predicting the amount of incoming solar radiation at these sensor nodes. We show that a Support Vector Machine (SVM) regression technique can effectively predict the amount of solar radiation for the next 24 hours based on weather data from previous days. We reveal that this technique outperforms other state of the art prediction methods for WSNs. To assess the performance of our proposed solution, we use experimental measurements that were collected for a period of two years from a weather station installed by Beijing Sunda Solar Energy Technology Company [2]. We also demonstrate how the harvested energy can be regulated using an innovative Power Management Unit [3]

Solar Generation Prediction using Artificial Intelligence: A Review

Solar energy generation is one of the most promising and fastest-growing renewable energy sources for the generation of useful energy worldwide. Forecasting of solar power is the most essential for the planning of grid operations, mainly in residential microgrids, to optimize and manage the energy produced in a dispatchable trend. Due to the inability of deterministic methods to accurately forecast solar power generation due to their dependency on natural inputs, Artificial Intelligence (AI) based techniques are required to be implemented.  AI techniques clubbed with stochastic methods are considered to be highly effective for solar generation forecasting. In this review, various artificial intelligence-based supervised and unsupervised learning methods for solar energy generation prediction are analyzed. The use of weather and environmental inputs for supervised learning is also compared. The accuracy of prediction of solar generation using several AI, Machine Learning, and Neural Network-based techniques are also analyzed in the paper. The paper presents an overall picture of the use of Artificial-Intelligence based techniques in solar generation prediction in the world

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

Day-Ahead Solar Resource Prediction Method Using Weather Forecasts for Peak Shaving

Due to recent concerns about energy sustainability, solar power is becoming more prevalent in distributed power generation. There are still obstacles which need to be addressed before solar power can be provided at the level of reliability that utilities require. Some of these issues can be mitigated with strategic use of energy storage. In the case of load shifting, energy storage can be used to supply solar energy during a time of day when utility customer\u27s demand is highest, thus providing partial peak load burden relief or peak shaving. Because solar resource availability is intermittent due to clouds and other atmospheric factors, charge/discharge planning must take weather into consideration. Many inter-day and intra-day solar resource prediction methods have been developed to aid in rm (high-reliability) resource establishment and peak-shaving through various methods and data sources with different levels of complexity. The purpose of this study was to investigate the use of readily-available, day-ahead National Weather Service (NWS) forecasts to develop a PV resource prediction. Using past day-ahead NWS weather forecasts and historical performance data from the Prosperity Energy Storage Project near Mesa del Sol in Albuquerque, New Mexico, several correlations were created based on regression analysis and optimized for minimal Root Mean Square (RMS) error for daily insolation prediction. Though some other methods such as the National Digital Forecast Database (NDFD) and Global Forecast System (GFS) exhibit greater accuracy, this method could prove to be a relatively simple means of planning the use of energy storage for peak-shaving or arbitrage. Additionally, given appropriate considerations for prediction uncertainty one could establish a rm resource to meet customer demand

Predicting Solar Irradiance in Singapore

Solar irradiance is the primary input for all solar energy generation systems. The amount of available solar radiation over time under the local weather conditions helps to decide the optimal location, technology and size of a solar energy project. We study the behaviour of incident solar irradiance on the earth's surface using weather sensors. In this paper, we propose a time-series based technique to forecast the solar irradiance values for shorter lead times of upto 15 minutes. Our experiments are conducted in the tropical region viz. Singapore, which receives a large amount of solar irradiance throughout the year. We benchmark our method with two common forecasting techniques, namely persistence model and average model, and we obtain good prediction performance. We report a root mean square of 147 W/m^2 for a lead time of 15 minutes.Comment: Published in Proc. Progress In Electromagnetics Research Symposium (PIERS), 201

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.Publicad

Integrated Geostationary Solar Energetic Particle Events Catalog: GSEP

We present a catalog of solar energetic particle (SEP) events covering solar cycles 22, 23 and 24. We correlate and integrate three existing catalogs based on Geostationary Operational Environmental Satellite (GOES) integral proton flux data. We visually verified and labeled each event in the catalog to provide a homogenized data set. We have identified a total of 341 SEP events of which 245 cross the space weather prediction center (SWPC) threshold of a significant proton event. The metadata consists of physical parameters and observables concerning the possible source solar eruptions, namely flares and coronal mass ejections for each event. The sliced time series data of each event, along with intensity profiles of proton fluxes in several energy bands, have been made publicly available. This data set enables researchers in machine learning (ML) and statistical analysis to understand the SEPs and the source eruption characteristics useful for space weather prediction