Search results for "Forecast"

Day-ahead forecasting for photovoltaic power using artificial neural networks ensembles

2016

Solar photovoltaic plants power output forecasting using machine learning techniques can be of a great advantage to energy producers when they are implemented with day-ahead energy market data. In this work a model was developed using a supervised learning algorithm of multilayer perceptron feedforward artificial neural network to predict the next twenty-four hours (day-ahead) power of a solar facility using fetched weather forecast of the following day. Each set of tested network configuration was trained by the historical power output of the plant as a target. For each configuration, one hundred networks ensembles was averaged to give the ability to generalize a better forecast. The train…

Unreliable predictions about COVID‐19 infections and hospitalizations make people worry: The case of Italy

2021

Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview

2021

Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of …

ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability.

2020

In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. Th…

Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception

2017

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A w…

JABB: Moving Towards The Future.

2012

Global Demand for Paper Products: 2006–2050

2012

Our aim is to formulate and present global demand forecasts for several paper products for the main regions of the world for the period 2005-2050. Our forecasts, while based on standard regression modeling, differ from existing ones in that they are based not only on historical observed consumption patterns and projections of economic growth, but also take into account changes in the demographic constitution of countries and regions, and incorporate the assumption that beyond certain level economic prosperity (here in terms of GDP per capita) does not translate into increased demand for paper products. Our key results are threefold. First, the demand for paperboard and hygiene products will…

Novel Energy Modelling and Forecasting Tools for Smart Energy Networks

2015

A novel Energy Modelling and Forecasting Tool (EMFT) has been adopted for use in the VIM SEN (Virtual Microgrids for Smart Energy Networks) project and this paper gives an insight of the techniques used to provide vital support to the energy market, in particular to energy aggregators. A brief description of one of the test sites where data has been collected for validation of the EMFT will be outlined and some examples shown. The information and predictions will then be used by a decision support system to dynamically adjust energy delivery and consumption, by giving advice to users and operators on actions they can take to obtain a better match between energy supply and demand that increa…

Machine learning methods to forecast temperature in buildings

2013

Efficient management of energy in buildings saves a very important amount of resources (both economic and technological). As a consequence, there is a very active research in this field. One of the keys of energy management is the prediction of the variables that directly affect building energy consumption and personal comfort. Among these variables, one can highlight the temperature in each room of a building. In this work we apply different machine learning techniques along with other classical ones for predicting the temperatures in different rooms. The obtained results demonstrate the validity of these techniques for predicting temperatures and, therefore, for the establishment of optim…

A Short-Term Data Based Water Consumption Prediction Approach

2019

A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With th…