Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Neural Network Ensembles for Time Series Prediction

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex non-linear relationships between multidimensional features and the time series outputs. In order to exploit these time-disparate relationships for the improved time series forecasting, the system requires a flexible methodology of combining multiple prediction models applied to multiple versions of the temporal data under significant noise component and variable temporal depth of predictions. In reply to this challenge a composite time series prediction model is proposed which combines the strength of multiple neural network (NN) regressors applied to the temporally varied feature subsets and the postprocessing smoothing of outputs developed to further reduce noise. The key strength of the model is its excellent adaptability and generalisation ability achieved through a highly diversified set of complementary NN models. The model has been evaluated within NISIS Competition 2006 and NN3 Competition 2007 concerning prediction of univariate and multivariate time-series. It showed the best predictive performance among 12 competitive models in the NISIS 2006 and is under evaluation within NN3 2007 Competition

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Improved streamflow forecasting using self-organizing radial basis function artificial neural networks

Streamflow forecasting has always been a challenging task for water resources engineers and managers and a major component of water resources system control. In this study, we explore the applicability of a Self Organizing Radial Basis (SORB) function to one-step ahead forecasting of daily streamflow. SORB uses a Gaussian Radial Basis Function architecture in conjunction with the Self-Organizing Feature Map (SOFM) used in data classification. SORB outperforms the two other ANN algorithms, the well known Multi-layer Feedforward Network (MFN) and Self-Organizing Linear Output map (SOLO) neural network for simulation of daily streamflow in the semi-arid Salt River basin. The applicability of the linear regression model was also investigated and concluded that the regression model is not reliable for this study. To generalize the model and derive a robust parameter set, cross-validation is applied and its outcome is compared with the split sample test. Cross-validation justifies the validity of the nonlinear relationship set up between input and output data. © 2004 Elsevier B.V. All rights reserved

A Multi-Stage Electricity Price Forecasting For Day-Ahead Markets

Forecasting hourly spot prices for real-time electricity usage is a challenging task. This thesis work investigates a series of price forecasting methods for day-ahead Iberian Electricity Markets (MIBEL). The dataset from MIBEL was used to train and test multiple forecast models. A hybrid combination of Auto Regressive Integrated Moving Average (ARIMA) and Generalized Linear Model (GLM) was proposed and its Mean Percentage Error (MAPE) values were compared against several methods. For example, ARIMA, GLM, Random forest (RF) and Support Vector Machines (SVM) methods are investigated. The results indicate a significant improvement in MAPE and correlation coefficient values for the proposed hybrid ARIMA-GLM method. Forecasting hourly spot prices for real-time electricity markets are key activities in energy trading operations. This thesis work specifically develop a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA, and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested with multiple duration periods ranging from one-week to ninety days for variables such as price, load, and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The results indicate a significant improvement in the Mean Absolute Percentage Error (MAPE) values compared to other present approaches. To reduce the prediction error, three types of variable selection techniques such as Relative importance using Linear Regression (LR), Multivariate Adaptive Regression Splines (MARS), and Random forest (RF) were used. Four datasets (Three months, Six months, weekday, and weekend) were used to validate the performance of the model. Three different set of variables (17, 4, 2) were used in this study. At last, three common variables selected from these feature selection approaches were tested with all these datasets. Considerable reduction in MAPE for both three and six-month dataset were achieved by these variable selection approaches. In addition, the work also investigate the application of a multi-layered deep neural network to the Iberian electric market (MIBEL) price forecasting task. A 3-month and 6-month of energy data are used to train the proposed model. The 3-month and 6-month period is treated as a historical dataset to train and predict the price for day-ahead markets. The network structure is implemented using Googleâs machine learning TensorFlow platform. Activation function such as Rectifier linear unit (ReLU) were tested to achieve a better Mean Absolute percentage error (MAPE) considering the weekday and weekend variations

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Ensemble Sales Forecasting Study in Semiconductor Industry

Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semiconductor industry, was considered. Past sale, future booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality and other indicators were innovatively incorporated into the quantitative modeling. Benefit from the recent advances in computation power and software development, millions of models built upon multiple regressions, time series analysis, random forest and boosting tree were executed in parallel. The models with smaller validation errors were selected to form the ensemble model. To better capture the distinct characteristics, forecasting models were implemented at lead time and lines of business level. The moving windows validation process automatically selected the models which closely represent current market condition. The weekly cadence forecasting schema allowed the model to response effectively to market fluctuation. Generic variable importance analysis was also developed to increase the model interpretability. Rather than assuming fixed distribution, this non-parametric permutation variable importance analysis provided a general framework across methods to evaluate the variable importance. This variable importance framework can further extend to classification problem by modifying the mean absolute percentage error(MAPE) into misclassify error. Please find the demo code at : https://github.com/qx0731/ensemble_forecast_methodsComment: 14 pages, Industrial Conference on Data Mining 2017 (ICDM 2017