Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Neural control of an autonomous robot

This article aims to apply the concepts associated with artificial neural networks (ANN) in the control of an autonomous robot system that is intended to be used in competitions of robots. The robot was tested in several arbitrary paths in order to verify its effectiveness. The results show that the robot performed the tasks with success. Moreover, in the case of arbitrary paths the ANN control outperforms other methodologies, such as fuzzy logic control (FLC).

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Comprehensive Expression Analyses of Neural Cell-Type-Specific miRNAs Identify New Determinants of the Specification and Maintenance of Neuronal Phenotypes.

MicroRNAs (miRNAs) have been shown to play important roles in both brain development and the regulation of adult neural cell functions. However, a systematic analysis of brain miRNA functions has been hindered by a lack of comprehensive information regarding the distribution of miRNAs in neuronal versus glial cells. To address this issue, we performed microarray analyses of miRNA expression in the four principal cell types of the CNS (neurons, astrocytes, oligodendrocytes, and microglia) using primary cultures from postnatal d 1 rat cortex. These analyses revealed that neural miRNA expression is highly cell-type specific, with 116 of the 351 miRNAs examined being differentially expressed fivefold or more across the four cell types. We also demonstrate that individual neuron-enriched or neuron-diminished RNAs had a significant impact on the specification of neuronal phenotype: overexpression of the neuron-enriched miRNAs miR-376a and miR-434 increased the differentiation of neural stem cells into neurons, whereas the opposite effect was observed for the glia-enriched miRNAs miR-223, miR-146a, miR-19, and miR-32. In addition, glia-enriched miRNAs were shown to inhibit aberrant glial expression of neuronal proteins and phenotypes, as exemplified by miR-146a, which inhibited neuroligin 1-dependent synaptogenesis. This study identifies new nervous system functions of specific miRNAs, reveals the global extent to which the brain may use differential miRNA expression to regulate neural cell-type-specific phenotypes, and provides an important data resource that defines the compartmentalization of brain miRNAs across different cell types.

Phenotypic heterogeneity of antigen-specific CD4 cells under different conditions of antigen persistence and antigen load

RESUME Nous n'avons pas de connaissance précise des facteurs à l'origine de l'hétérogénéité phénotypique des cellules T CD4 mémoires. Une troisième population phénotypique des cellules T CD4 mémoires, caractérisée par les marqueurs CD45RA+CCR7- a été identifiée dans cette étude. Cette population présente un état de différentiation avancée, comme en témoigne son histoire de réplication, ainsi que sa capacité de prolifération homéostatique. Les réponses des cellules T CD4 mémoires à différentes conditions de persistance et charge antigénique ont trois patterns phénotypiques différents, caractérisés par les marqueurs CD45RA et CCR7. La réponse CD4 mono -phénotypique CD45RA-CCR7+ ou CD45RA- CCR7- est associée à des conditions d'élimination de l'antigène (telle la réponse CD4 tétanos spécifique) ou à des conditions de persistance antigénique et de virémie élevée (telle la réponse HIV chronique ou la primo-infection CMV) respectivement. D'autre part, les réponses T CD4 multi -phénotypiques CD45RA-CCR7+ sont associées à des conditions d'exposition antigénique prolongée et de faible virémie (telles les infections CMV, EBV et HSV ou les infections HIV chez les long term non progressons). La réponse mono -phénotypique CD45RA- CCR7+ est propre aux cellules T CD4 secrétant de IL2, définies également comme centrales mémoires, la réponse CD45RA- CCR7- aux cellules T CD4 secrétant de l'IFNγ et finalement la réponse mufti-phénotypique aux cellules T CD4 secrétant à la fois de l'IL2 et de l' IFNγ. En conclusion, ces résultats témoignent d'une régulation de l'hétérogénéité phénotypique par l'exposition et la charge antigénique. ABSTRACT The factors responsible for the phenotypic heterogeneity of memory CD4 T cells are unclear. In the present study, we have identified a third population of memory CD4 T cells characterized as CD45RA+CCRT that, based on its replication history and the homeostatic proliferative capacity, was at an advanced stage of differentiation. Three different phenotypic patterns of memory CD4 T cell responses were delineated under different conditions of antigen (Ag) persistence and load using CD45RA and CCR7 as markers of memory T cells. Mono-phenotypic CD45RA'CCR7+ or CD45RA'CCR7' CD4 T cell responses were associated with conditions of Ag clearance (tetanus toxoid-specific CD4 T cell response) or Ag persistence and high load (chronic HIV-1 and primary CMV infections), respectively. Multi-phenotypic CD45RA CCR7+, CD45RA'CCRT and CD45RA+CCRT CD4 T cell responses were associated with protracted Ag exposure and low load (chronic CMV, EBV and HSV infections and HIV-1 infection in long-term nonprogressors). The mono-phenotypic CD45RA'CCR7+ response was typical of central memory (TCM) IL-2-secreting CD4 T cells, the mono-phenotypic CD45RA CCRT response of effector memory (TEM) IFN-γ -secreting CD4 T cells and the multi-phenotypic response of both IL-2- and IFN-γ -secreting cells. The present results indicate that the heterogeneity of different Ag-specific CD4 T cell responses is regulated by Ag exposure and Ag load.

Heterogeneity of atherosclerotic plaque phenotypes and composition in four different arterial beds: an intravascular ultrasound virtual histology study

Purpose: The purpose of this study was to compare the plaque morphology between coronary and peripheral arteries using intravascular ultrasound (IVUS). Methods: IVUS was performed in 68 patients with coronary and 93 with peripheral artery lesions (29 carotid, 50 renal, and 14 iliac). Plaques were classified as fibroatheroma (VH-FA) (further subclassified as thin-capped [VH-TCFA] and thick-capped [VH-ThCFA]), fibrocalcific plaque (VH-FC) and pathological intimal thickening (VH-PIT). Results: Plaque rupture (13% of coronary, 7% of carotid, 6% of renal, and 7% of iliac arteries; P=NS) and VH-TCFA (37% of coronary, 24% of carotid, 16% of renal, and 7% of iliac arteries; P=0.02) was observed in all arteries. Compared to coronary arteries, VH-FA was less frequently observed in renal (P<0.001) and iliac arteries (P<0.006), while VH-PIT and VH-FC were prevalent in both of these peripheral arteries. Lesions with positive remodeling demonstrated more characteristics of VH-FA in coronary, carotid, and renal arteries compared to those with intermediate/negative remodeling (all P<0.01). There was positive relationship between RI and percent necrotic core area in all four arteries. Conclusions: Atherosclerotic plaque phenotypes were heterogeneous among four different arteries. In contrast, the associations of remodeling mode with plaque phenotype and composition were similar among the various arterial beds.

Identifying the neural correlates of executive functions in early cerebral microangiopathy: a combined VBM and DTI study.

Cerebral microangiopathy (CMA) has been associated with executive dysfunction and fronto-parietal neural network disruption. Advances in magnetic resonance imaging allow more detailed analyses of gray (e.g., voxel-based morphometry-VBM) and white matter (e.g., diffusion tensor imaging-DTI) than traditional visual rating scales. The current study investigated patients with early CMA and healthy control subjects with all three approaches. Neuropsychological assessment focused on executive functions, the cognitive domain most discussed in CMA. The DTI and age-related white matter changes rating scales revealed convergent results showing widespread white matter changes in early CMA. Correlations were found in frontal and parietal areas exclusively with speeded, but not with speed-corrected executive measures. The VBM analyses showed reduced gray matter in frontal areas. All three approaches confirmed the hypothesized fronto-parietal network disruption in early CMA. Innovative methods (DTI) converged with results from conventional methods (visual rating) while allowing greater spatial and tissue accuracy. They are thus valid additions to the analysis of neural correlates of cognitive dysfunction. We found a clear distinction between speeded and nonspeeded executive measures in relationship to imaging parameters. Cognitive slowing is related to disease severity in early CMA and therefore important for early diagnostics.