Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

A Review of Short Term Load Forecasting using Artificial Neural Network Models

AbstractThe electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly

A novel genetic-algorithm-based neural network for short-term load forecasting

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network

Short-term hourly load forecasting in South Africa using neutral networks

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science, Johannesburg, 30 March 2018.Accuracy of the load forecasts is very critical in the power system industry, which is the lifeblood of the global economy to such an extent that its art-of-the-state management is the focus of the Short-Term Load Forecasting (STLF) models. In the past few years, South Africa faced an unprecedented energy management crisis that could be addressed in advance, inter alia, by carefully forecasting the expected load demand. Moreover, inaccurate or erroneous forecasts may result in either costly over-scheduling or adventurous under-scheduling of energy that may induce heavy economic forfeits to power companies. Therefore, accurate and reliable models are critically needed. Traditional statistical methods have been used in STLF but they have limited capacity to address nonlinearity and non-stationarity of electric loads. Also, such traditional methods cannot adapt to abrupt weather changes, thus they failed to produce reliable load forecasts in many situations. In this research report, we built a STLF model using Artificial Neural Networks (ANNs) to address the accuracy problem in this field so as to assist energy management decisions makers to run efficiently and economically their daily operations. ANNs are a mathematical tool that imitate the biological neural network and produces very accurate outputs. The built model is based on the Multilayer Perceptron (MLP), which is a class of feedforward ANNs using the backpropagation (BP) algorithm as its training algorithm, to produce accurate hourly load forecasts. We compared the MLP built model to a benchmark Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model using data from Eskom, a South African public utility. Results showed that the MLP model, with percentage error of 0.50%, in terms of the MAPE, outperformed the SARIMAX with 1.90% error performance.LG201