An Overview of Electricity Demand Forecasting Techniques

Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year.  Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Essays in econometrics

This dissertation contributes to the theoretical understanding and practical application of non- and semi-parametric methods in econometrics. It consists of three chapters. The first chapter advocates the use of unsupervised statistical learning (clustering) techniques to group observations from a series of repeated cross-sections to create a pseudo-panel of group averages. This clustering method is based on features of the data space and does not require external grouping variables unlike many other methods. Using a model of enterprise training as an example, fixed eff ects panel data model is estimated using a pseudo-panel of cluster centers. Chapters 2 and 3 extend univariate kernel methods to the estimation of time-varying distributions and densities subject to moment constraints. Chapter 2 proposes a weighted kernel density estimator for a time-varying probability density function and the corresponding cumulative distribution function. Time-varying quantiles are estimated by inverting an estimate of the cumulative distribution function. Weighting schemes are derived from those used in time series modelling. Parameters, including the bandwidth, may be estimated by maximum likelihood or cross-validation. Diagnostic checks are constructed based on residuals given by the predictive cumulative distribution function. Chapter 3 considers a set-up where additional information concerning the distribution of random variables is available in the form of moment conditions. A weighted kernel density estimate reflecting the extra information is constructed by replacing the uniform weights associated with standard kernel density estimator by generalised empirical likelihood implied probabilities. This chapter shows that the resulting density estimator provides an improved approximation to the moment conditions. Moreover, a reduction in variance is achieved due to the systematic use of the extra moment information

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Optical Classification of an Urbanized Estuary Using Hyperspectral Remote Sensing Reflectance

Optical water classification based on remote sensing reflectance (Rrs(.)) data can provide insight into water components driving optical variability and inform the development and application of bio-optical algorithms in complex aquatic systems. In this study, we use an in situ dataset consisting of hyperspectral Rrs(.) and other biogeochemical and optical parameters collected over nearly five years across a heavily urbanized estuary, the Long Island Sound (LIS), east of New York City, USA, to optically classify LIS waters based on Rrs(.) spectral shape. We investigate the similarities and differences of discrete groupings (k-means clustering) and continuous spectral indexing using the Apparent Visible Wavelength (AVW) in relation to system biogeochemistry and water properties. Our Rrs(.) dataset in LIS was best described by three spectral clusters, the first two accounting for the majority (89%) of Rrs(.) observations and primarily driven by phytoplankton dynamics, with the third confined to measurements in river and river plume waters. We found AVW effective at tracking subtle changes in Rrs(.) spectral shape and fine-scale water quality features along river-to-ocean gradients. The recently developed Quality Water Index Polynomial (QWIP) was applied to evaluate three different atmospheric correction approaches for satellite-derived Rrs(.) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor in LIS, finding Polymer to be the preferred approach. Our results suggest that integrative, continuous indices such as AVW can be effective indicators to assess nearshore biogeochemical variability and evaluate the quality of both in situ and satellite bio-optical datasets, as needed for improved ecosystem and water resource management in LIS and similar regions

Electricity Spot Price Forecast by Modelling Supply and Demand Curve

Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This research received no external fundingElectricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.publishersversionpublishe

Multivariate Statistical Methodologies used in In-vitro Raman Spectroscopy: Simulations and Applications for Drug and Nanoparticle Interactions

Raman spectroscopy is a growing technology in the fields of in-vitro drug and nanoparticle screening. The label free capability provided by vibrational spectroscopy, as well as the ability of the technique to probe the chemical nature of samples, makes it a good candidate for use in these fields. Crucial to the progress of these methods is the development and validation of robust and accurate multivariate statistical analysis protocols. In this thesis, both established and novel methods are examined using both real and simulated datasets. In particular, simulated datasets are used to validate and assess the accuracy of these methods in a spectroscopic setting. Firstly, partial least squares regression (PLSR) is examined using a simulated model based on real experimental data. This is applied to investigate the application of the algorithm to continuously varying data with known spectral perturbations introduced over a range of concentrations and responses. The results show that, while PLSR is valid for some dose ranges, sub-lethal, low concentrations and thus subtle spectral changes in the data may lead to difficulties in model construction. Multiple trends present in the data were also investigated and possible model error based on spectral bleedthrough in the regression coefficients RCs is explored. Principal component analysis (PCA) was also investigated using simulated datasets based on known changes in the data. Some of the limitations of PCA for data partitioning and trend analysis are overcome by a novel variant termed, ‘seeded’ PCA. 1st and 2nd derivative data is also explored for improvements in Raman spectral analysis using seeded PCA