A hybridwind speed forecasting system based on a 'decomposition and ensemble' strategy and fuzzy time series

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Accurate and stable wind speed forecasting is of critical importance in the wind power industry and has measurable influence on power-system management and the stability of market economics. However, most traditional wind speed forecasting models require a large amount of historical data and face restrictions due to assumptions, such as normality postulates. Additionally, any data volatility leads to increased forecasting instability. Therefore, in this paper, a hybrid forecasting system, which combines the 'decomposition and ensemble' strategy and fuzzy time series forecasting algorithm, is proposed that comprises two modules-data pre-processing and forecasting. Moreover, the statistical model, artificial neural network, and Support Vector Regression model are employed to compare with the proposed hybrid system, which is proven to be very effective in forecasting wind speed data affected by noise and instability. The results of these comparisons demonstrate that the hybrid forecasting system can improve the forecasting accuracy and stability significantly, and supervised discretization methods outperform the unsupervised methods for fuzzy time series in most cases

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

A decision support system for wind power production

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance

Contributions for microgrids dynamic modelling and operation

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Machine Learning based Wind Power Forecasting for Operational Decision Support

To utilize renewable energy efficiently to meet the needs of mankind's living demands becomes an extremely hot topic since global warming is the most serious global environmental problem that human beings are facing today. Burning of fossil fuels, such as coal and oil directly for generating electricity leads to environment pollution and exacerbates global warning. However, large-scale development of hydropower increases greenhouse gas emissions and greenhouse effects. This research is related to knowledge of wind power forecasting (WPF) and machine learning (ML). This research is built around one central research question: How to improve the accuracy of WPF by using AI methods? A pilot conceptual system combining meteorological information and operations management has been formulated. The main contribution is visualized in a proposed new framework, named Meteorological Information Service Decision Support System, consisting of a meteorological information module, wind power prediction module and operations management module. This conceptual framework has been verified by quantitative analysis in empirical cases. This system utilizes meteorological information for decision-making based on condition-based maintenance in operations and management for the purpose of optimizing energy management. It aims to analyze and predict the variation of wind power for the next day or the following week to develop scheduling planning services for WPEs based on predicting wind speed for every six hours, which is short-term wind speed prediction, through training, validating, and testing dataset. Accurate prediction of wind speed is crucial for weather forecasting service and WPF. This study presents a carefully designed wind speed prediction model which combines fully-connected neural network (FCNN), long short-term memory (LSTM) algorithm with eXtreme Gradient Boosting (XGBoost) technique, to predict wind speed. The performance of each model is tested by using reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) for Meteorological observatory located in Vaasa in Finland. The results show that XGBoost algorithm has similar improved prediction performance as LSTM algorithm, in terms of RMSE, MAE and R2 compared to the commonly used traditional FCNN model. On the other hand, the XGBoost algorithm has a significant advantage on training time while comparing to the other algorithms in this case study. Additionally, this sensitivity analysis indicates great potential of the optimized deep learning (DL) method, which is a subset of machine learning (ML), in improving local weather forecast on the coding platform of Python. The results indicate that, by using Meteorological Information Service Decision Support System, it is possible to support effective decision-making and create timely actions within the WPEs. Findings from this research contribute to WPF in WPEs. The main contribution of this research is achieving decision optimization on a decision support system by using ML. It was concluded that the proposed system is very promising for potential applications in wind (power) energy management