Day-ahead electricity price forecasting using optimized multiple-regression of relevance vector machines

In deregulated, auction-based, electricity markets price forecasting is an essential participant tool for developing bidding strategies. In this paper, a day-ahead intelligent forecasting method for electricity prices is presented. The proposed approach is comprised of two steps. In the first step, a set of two relevance vector machines (RVM) is employed where each one provides next day predictions for the price evolution. In the second step, a multiple regression model comprised of the two relevance vector machines is built and the regression coefficients are computed using genetic based optimization. The performance of the proposed approach is tested on a set of electricity price hourly data from four different seasons and compared to those obtained by each of the relevance vector machines. The results clearly demonstrate, in terms of mean square error, the superiority of the proposed method over each individual RVM

Application of fuzzy multiplexing of learning Gaussian processes for the interval forecasting of wind speed

Robust forecasting of wind speed values is a key element to effectively accommodate renewable generation from wind in smart power systems. However, the stochastic nature of wind and the uncertainties associated with it impose high challenge in its forecasting. A new method for forecasting wind speed in renewable energy generation is introduced in this study. The goal of the method is to provide a forecast in the form of an interval, which is determined by a mean value and the variance around the mean. In particular, the forecasting interval is produced according to a two‐step process: in the first step, a set of individual kernel modelled Gaussian processes (GP) are utilised to provide a respective set of interval forecasts, i.e. mean and variance values, over the future values of the wind. In the second step, the individual forecasts are evaluated using a fuzzy driven multiplexer, which selects one of them. The final output of the methodology is a single interval that has been identified as the best among the GP models. The presented methodology is tested on the set of real‐world data and benchmarked against the individual GPs as well as the autoregressive moving average model

Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power

This article describes how the integration of renewable energy in the power grid is a critical issue in order to realize a smart grid infrastructure. To that end, intelligent methods that monitor and currently predict the values of critical variables of renewable energy are essential. With respect to wind power, such variable is the wind speed given that it is of great interest to efficient schedule operation of a wind farm. In this article, a new methodology for predicting wind speed is presented for very short-term prediction horizons. The methodology integrates multiple Gaussian process regressors (GPR) via the adoption of an optimization problem whose solution is given by the particle swarm optimization algorithm. The optimized framework is utilized for the average hourly wind speed prediction for a prediction horizon of six hours ahead. Results demonstrate the ability of the methodology in accurately forecasting the wind speed. Furthermore, obtained forecasts are compared with those taken from single Gaussian process regressors as well from the integration of the same multiple GPR using a genetic algorithm

Learning Uncertainty of Wind Speed Forecasting Using a Fuzzy Multiplexer of Gaussian Processes

The smart power systems of the future will be able to accommodate wind power at a maximum efficiency by utilizing available information. For instance, information pertained to wind speed is essential in forecasting the overall amount of power generated by wind farms. Information is used to offset the inherent stochasticity of wind power and improve wind speed forecasting precision. In this work, an intelligent methodology for quantifying the uncertainty of wind speed pertained to forecasting is introduced. The introduced methodology adopts a set of Gaussian processes to assemble a model of the uncertainty of the forecasted speed. Results are taken on a set of real-world wind speed data

Extreme Interval Electricity Price Forecasting of Wholesale Markets Integrating ELM and Fuzzy Inference

The electricity wholesale market is inherently volatile in a deregulated market structure where market participants like power generators and retailors drive the price of electricity. Timely forecasting of the wholesale market prices by market participants has become of utmost importance in order to maximize on profits and minimize on risks. This report presents a hybrid method comprised of an extreme learning machine and a fuzzy inference engine to forecast price intervals using historical wholesale price extreme values (price maximum and minimum), historical load, generation and congestion hours, forecasted temperature and power outage data. This hybrid forecasting method has been tested on RTO Pennsylvania-New Jersey-Maryland (PJM) interconnection for the period July 1st, 2018 to February 8th, 2019, and is compared with individual extreme learning machine and the non-linear autoregressive neural network. © 2019 IEEE