Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

The Fate of Herbicides in Soil

The prediction of the movement and fate of herbicides in soils represents an important startegy in limiting their environmental impact. The chemico-physical properties of herbicides affect thier behaviour in soil and regulate their interaction mechanisms with organic and inorganic soil phases. Among these, dissolved organic matter plays an important role bacause it influences the mobility of herbicides by complex interactions that can facilitate or reduce the movement of chemicals along the soil profile. The knowledge of soil phase characteristics and the mechanisms involved in herbicide transformation can help to understand the fate of herbicides in soil

Day-ahead PV power forecast by hybrid ANN compared to the five parameters model estimated by particle filter algorithm

A comparison between the hybrid method (PHANN – Physical Hybrid Artificial Neural Network) and the 5 parameter Physical model, which have been determined by the particle filter algorithm, is presented here. These methods have been employed to perform the dayahead forecast of the output power of a photovoltaic plant. The aim of this work is to assess the forecast accuracy of the two methods

ANN sizing procedure for the day-ahead output power forecast of a PV plant

Since the beginning of this century, the share of renewables in Europe's total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance

The optimum PV plant for a given solar DC/AC converter

In recent years, energy production by renewable sources is becoming very important, and photovoltaic (PV) energy has became one of the main renewable sources that is widely available and easily exploitable. In this context, it is necessary to find correct tools to optimize the energy production by PV plants. In this paper, by analyzing available solar irradiance data, an analytical expression for annual DC power production for some selected places is introduced. A general efficiency curve is extracted for different solar inverter types, and by applying approximated function, a new analytical method is proposed to estimate the optimal size of a grid-connected PV plant linked up to a specific inverter from the energetic point of view. An exploitable energy objective function is derived, and several simulations for different locations have been provided. The derived analytical expression contains only the available data of the inverter (such as efficiency, nominal power, etc.) and the PV plant characteristics (such as location and PV nominal power)

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic

Alcohol and older people: the European project VINTAGE: good health into older age; design, methods and major results

Consum d'alcohol; Persones grans; Disseny de recercaConsumo de alcohol; Personas mayores; Diseño de investigaciónAlcohol drinking; Aged; Research designObjectives: The European project VINTAGE – Good Health Into Older Age aims at filling the knowledge gap and building capacity on alcohol and the elderly, encouraging evidence- and experience-based interventions. Methods: Systematic review of scientific literature on the impact of alcohol on older people; ad hoc survey and review of grey literature to collect EU examples of good practices for prevention; dissemination of findings to stakeholders involved in the field of alcohol, aging or public health in general. Results: Design and procedures of the VINTAGE project are described, providing also an outline of major results, with particular attention to those related to the dissemination activity. Conclusions: Much more information and research is needed. This issue should be part of both alcohol and healthy ageing policies.The VINTAGE project “Good Health into Older Age” is a project funded by the Executive Agency for Health and Consumers, under the European Commission Second Programme of Community Action in the Field of Health 2008-2013 (Grant Agreement no. 20081203)

Parthenolide induces caspase-independent and AIF-mediated cell death in human osteosarcoma and melanoma cells.

The mechanism of the cytotoxic effect exerted by parthenolide on tumor cells is not clearly defined today. This article shows that parthenolide stimulates in human osteosarcoma MG63 and melanoma SK-MEL-28 cells a mechanism of cell death, which is not prevented by z-VAD-fmk and other caspase inhibitors. In particular treatment with parthenolide rapidly stimulated (1-2 h) reactive oxygen species (ROS) generation by inducing activation of extracellular signal-regulated kinase 1/2 (ERK 1/2) and NADPH oxidase. This event caused depletion of thiol groups and glutathione, NF-κB inhibition, c-Jun N-terminal kinase (JNK) activation, cell detachment from the matrix, and cellular shrinkage. The increase of ROS generation together with the mitochondrial accumulation of Ca(2+) also favored dissipation of Δψm, which seemed primarily determined by permeability transition pore opening, since Δψm loss was partially prevented by the inhibitor cyclosporin A. Staining with Hoechst 33342 revealed in most cells, at 3-5 h of treatment, chromatin condensation, and fragmentation, while only few cells were propidium iodide (PI)-positive. In addition, at this stage apoptosis inducing factor (AIF) translocated to the nucleus and co-localized with areas of condensed chromatin. Prolonging the treatment (5-15 h) ATP content declined while PI-positive cells strongly augmented, denouncing the increase of necrotic effects. All these effects were prevented by N-acetylcysteine, while caspase inhibitors were ineffective. We suggest that AIF exerts a crucial role in parthenolide action. In accordance, down-regulation of AIF markedly inhibited parthenolide effect on the production of cells with apoptotic or necrotic signs. Taken together our results demonstrate that parthenolide causes in the two cell lines a caspase-independent cell death, which is mediated by AIF