Distribution-Based Categorization of Classifier Transfer Learning

Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. Due to its relevance, TL has gained significant interest in the Machine Learning community since it paves the way to devise intelligent learning models that can easily be tailored to many different applications. As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far. However, a wide range of works have been reporting different names for the same concepts. This concept and terminology mixture contribute however to obscure the TL field, hindering its proper consideration. In this paper we present a review of the literature on the majority of classification TL methods, and also a distribution-based categorization of TL with a common nomenclature suitable to classification problems. Under this perspective three main TL categories are presented, discussed and illustrated with examples

Risk Analysis and Behavior of Electricity Portfolio Aggregator

The scope of this paper is to adapt the standard mean-variance model of Henry Markowitz theory, creating a simulation tool to find the optimal configuration of the portfolio aggregator, calculate its profitability and risk. Currently, there is a deep discussion going on among the power system society about the structure and architecture of the future electric system. In this environment, policy makers and electric utilities find new approaches to access the electricity market; this configures new challenging positions in order to find innovative strategies and methodologies. Decentralized power generation is gaining relevance in liberalized markets, and small and medium size electricity consumers are also become producers (“prosumers”). In this scenario an electric aggregator is an entity that joins a group of electric clients, customers, producers, “prosumers” together as a single purchasing unit to negotiate the purchase and sale of electricity. The aggregator conducts research on electricity prices, contract terms and conditions in order to promote better energy prices for their clients and allows small and medium customers to benefit improved market prices

Commercial agents portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk

Commercial agentes portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk

Price forecasting in the day-ahead Iberian electricity market using a conjectural variations Arima model

Price forecast is a matter of concern for all participants in electricity markets, from suppliers to consumers through policy makers, which are interested in the accurate forecast of day-ahead electricity prices either for better decisions making or for an improved evaluation of the effectiveness of market rules and structure. This paper describes a methodology to forecast market prices in an electricity market using an ARIMA model applied to the conjectural variations of the firms acting in an electricity market. This methodology is applied to the Iberian electricity market to forecast market prices in the 24 hours of a working day. The methodology was then compared with two other methodologies, one called naive and the other a direct forecast of market prices using also an ARIMA model. Results show that the conjectural variations price forecast performs better than the naive and that it performs slightly better than the direct price forecast

How market power affects the behaviour of a pumped storage hydro unit in the day-ahead electricity market?

The integration of large amounts of wind energy in power systems raises important operation issues such as the balance between power demand and generation. The pumped storage hydro (PSH) units are seen as one solution for this issue, avoiding the need for wind power curtailments. However, the behavior of a PSH unit might differ considerably when it operates in a liberalized market with some degree of market power. In this regard, a new approach for the optimal daily scheduling of a PSH unit in the day-ahead electricity market was developed and presented in this paper, in which the market power is modeled by a residual inverse demand function with a variable elasticity. The results obtained show that increasing degrees of market power of the PSH unit correspond to decreasing levels of storage and, therefore, the capacity to integrate wind power is considerably reduced under these circumstances

Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition

In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles. Immunogold particles are part of a high-magnification method for the selective localization of biological molecules at the subcellular level only visible through Electron Microscopy. The number of immunogold particles in the cell walls allows the assessment of the differences in their compositions providing a tool to analise the quality of different plants. For its quantization one requires a laborious manual labeling (or annotation) of images containing hundreds of particles. The system that is proposed in this paper can leverage significantly the burden of this manual task. For particle detection we use a LoG filter coupled with a SDA. In order to improve the recognition, we also study the applicability of TL settings for immunogold recognition. TL reuses the learning model of a source problem on other datasets (target problems) containing particles of different sizes. The proposed system was developed to solve a particular problem on maize cells, namely to determine the composition of cell wall ingrowths in endosperm transfer cells. This novel dataset as well as the code for reproducing our experiments is made publicly available. We determined that the LoG detector alone attained more than 84\% of accuracy with the F-measure. Developing immunogold recognition with TL also provided superior performance when compared with the baseline models augmenting the accuracy rates by 10\%

Is the electric vehicle a solution for the wind power integration in the portuguese power system?

The integration of Plug-in electric vehicles in the transportation sector has a great potential to reduce oil dependency, the GHG emissions and to contribute for the integration of renewable sources into the electricity generation mix. Portugal has a high share of wind energy, and curtailment may occur, especially during the off-peak hours with high levels of hydro generation. In this context, the electric vehicles, seen as a distributed storage system, can help to reduce the potential wind curtailments and, therefore, increase the integration of wind power into the power system. In order to assess the energy and environmental benefits of this integration, a methodology based on a unit commitment and economic dispatch is adapted and implemented. From this methodology, the thermal generation costs, the CO2 emissions and the potential wind generation curtailment are computed. Simulation results show that a 10% penetration of electric vehicles in the Portuguese fleet would increase electrical load by 3% and reduce wind curtailment by only 26%. This results from the fact that the additional generation required to supply the electric vehicles is mostly thermal. The computed CO2 emissions of the EV are 92 g CO2/kWh which become closer to those of some new ICE engines