Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques

AbstractWith increased penetration of solar as a variable energy resource (VER), solar photovoltaic (PV) power production is rapidly increasing into large-scale power industries. Since power output of PV systems depends critically on the weather, unexpected variations of their power output may increase the operating costs of the power system. Moreover, a major barrier in integrating this VER into the grid is its unpredictability, since steady output cannot be guaranteed at any particular time. This biases power utilities against using PV power since the planning and overall balancing of the grid becomes very challenging. Developing a reliable algorithm that can minimize the errors associated with forecasting the near future PV power generation is extremely beneficial for efficiently integrating VER into the grid. PV power forecasting can play a key role in tackling these challenges. This paper presents one-hour-ahead power output forecasting of a PV system using a combination of wavelet transform (WT) and artificial intelligence (AI) techniques by incorporating the interactions of PV system with solar radiation and temperature data. In the proposed method, the WT is applied to have a significant impact on ill-behaved PV power time-series data, and AI techniques capture the nonlinear PV fluctuation in a better way

Online Sensorless Solar Power Forecasting for Microgrid Control and Automation

Meteorological conditions such as air density, temperature, solar radiation etc. strongly affect the power generation from solar, and thus, the prediction and estimation process should consider weather conditions as critical inputs. The nature of weather forecast is highly unpredictable, so many applications use meteorological data from in-place on-site sensors to add to the forecast and some use complex networks with complicated mapping. The in-situ sensor approach and dense mapping methods, however, present several drawbacks. First, the use of sensors give rise to extra operational, installation and maintenance cost. Second, it requires significant amount of time to capture and accumulate data for various occasions and scenarios, and in addition, sensor itself can be the cause of error measurements. The complex methods are computational inefficient and may present suboptimal convergence. This paper presents a sensorless solar output power forecasting based on historical weather (publicly available from met office) and PV data. The algorithm uses simple to implement neural networks with few neurons and hidden layers for its training and allows for day a head forecast. The proposed methodology presents a guideline on how to select the relevant data from weather and how it affects the accuracy and training time of neural network. The benefit of developed method is an improvement on the energy management, utilization and reliability of the microgrid

An intelligent solar powered battery buffered EV charging station with solar electricity forecasting and EV charging load projection functions

An intelligent energy management approach for a solar-powered EV charging station with energy storage has been studied and demonstrated for a level 2 charger at the University of California-Davis West Village. The approach introduces solar PV electrical energy forecasting and EV charging demand projection to optimize the energy management of the charging station. The percentage of cloud cover is extracted from a weather forecast website for estimating the available PV electrical energy. A linear fit of the historical EV charging load from the same day of the week over the previous six weeks is employed for extracting the charging pattern of the workplace EV charging station. Both simulations and actual operation show that intelligent energy management for a charging station with a buffer battery can reduce impacts of the EV charging system on utility grids in terms of peak power demand and energy exchange, reduce grid system losses, and benefit the charging station owner through the Time-of-Use rate plans

Output Power Forecasting for 2kW Monocrystalline PV System using Response Surface Methodology

 Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic is used. The 5 minute sampling size of yearly 2014 weather station data the three environmental elements and output power of a 2kW Monocrystalline real PV system are used for training. Whereas, yearly 2015 data of the aforementioned elements are used for validation. The coefficient of determination (R2) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results show that, full quadratic is the most accurate prediction model with R2 value of 0.9995 and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer.

Using a nonparametric PV model to forecast AC power output of PV plants

In this paper, a methodology using a nonparametric model is used to forecast AC power output of PV plants using as inputs several forecasts of meteorological variables from a Numerical Weather Prediction (NWP) model and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast the AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, and results show that the daily production of individual plants can be predicted with a skill score up to 0.361

A short-term photovoltaic power prediction model based on an FOS-ELM algorithm

With the increasing proportion of photovoltaic (PV) power in power systems, the problem of its fluctuation and intermittency has become more prominent. To reduce the negative influence of the use of PV power, we propose a short-term PV power prediction model based on the online sequential extreme learning machine with forgetting mechanism (FOS-ELM), which can constantly replace outdated data with new data. We use historical weather data and historical PV power data to predict the PV power in the next period of time. The simulation result shows that this model has the advantages of a short training time and high accuracy. This model can help the power dispatch department schedule generation plans as well as support spatial and temporal compensation and coordinated power control, which is important for the security and stability as well as the optimal operation of power systems

Output Power Forecasting For 2kW Monocrystalline PV System Using Response Surface Methodology

Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, back module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic were used. The 5 minute sampling size of year 2014 weather station data of the three environmental elements and output power of a 2kW Monocrystalline real PV system were used for training. Whereas, year 2015 data of the aforementioned elements were used for validation. The coefficient of determination (R2) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results shown that, full quadratic is the most accurate prediction model with R2 value of 0.9995 and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer

A Data Driven Approach to Solar Generation Forecasting

With the developments in renewable energy resources, more Photovoltaic (PV) generators are being built. Compared to traditional generators, a PV generator is less controllable which will adversely impact power system operation and planning. To ensue seamless operation of power systems, PV forecasting is essential and necessary. A challenge in PV forecasting is that PV generation behavior differs in different regions due to the fact that PV generation is highly dependent on weather conditions, in particular solar irradiance. This makes it important to study the power output data based on a specific region. In this thesis, I first analyze how PV forecasting will affect system planning by calculating probabilistic power flow (PPF). By using a variety of probabilistic models that can estimate solar irradiance, the PPF of each model is calculated and compared. The PPF will give us an idea of how accurate and inaccurate forecasts will affect power system operations and planning. I then seek to find out which method can forecast the power output more accurately. I used several methods such as Linear Regression, Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), and compared these methods in 3-hours ahead forecasting. In addition, these methods are analyzed for future use, as the dataset used is constantly growing. Through my analysis of the data, I found out that, based on a small dataset, linear regression works better and as the dataset grows larger, the error for K-Nearest Neighbor reduces dramatically. In addition, a new approach named Symbolic Aggregate approximation (SAX) was used when an extremely large dataset was used to increase calculation speed and reduce dimensionality