Luis Hernandez

Bio: Luis Hernandez is an academic researcher from Carlos III Health Institute. The author has contributed to research in topics: Delta-sigma modulation & Voltage-controlled oscillator. The author has an hindex of 21, co-authored 135 publications receiving 2126 citations. Previous affiliations of Luis Hernandez include University of Waterloo & Lantiq.

A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings

Abstract: Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the `70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.

A 70-mW 300-MHz CMOS continuous-time /spl Sigma//spl Delta/ ADC with 15-MHz bandwidth and 11 bits of resolution

Abstract: A wide-bandwidth continuous-time sigma-delta ADC is implemented in a 0.13-/spl mu/m CMOS. The circuit is targeted for wide-bandwidth applications such as video or wireless base-stations. The active blocks are composed of regular threshold voltage devices only. The fourth-order architecture uses an OpAmp-RC-based loop filter and a 4-bit internal quantizer operated at 300-MHz clock frequency. The converter achieves a dynamic range of 11 bits over a bandwidth of 15 MHz. The power dissipation is 70 mW from a 1.5-V supply.

A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants

Abstract: Recent technological advances in the power generation and information technologies areas are helping to change the modern electricity supply system in order to comply with higher energy efficiency and sustainability standards. Smart grids are an emerging trend that introduce intelligence in the power grid to optimize resource usage. In order for this intelligence to be effective, it is necessary to retrieve enough information about the grid operation together with other context data such as environmental variables, and intelligently modify the behavior of the network elements accordingly. This article presents a multi-agent system model for virtual power plants, a new power plant concept in which generation no longer occurs in big installations, but is the result of the cooperation of smaller and more intelligent elements. The proposed model is not only focused on the management of the different elements, but includes a set of agents embedded with artificial neural networks for collaborative forecasting of disaggregated energy demand of domestic end users, the results of which are also shown in this article.

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Abstract: Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

Diamondback Moth Ecology and Management: Problems, Progress, and Prospects

Abstract: Agricultural intensification and greater production of Brassica vegetable and oilseed crops over the past two decades have increased the pest status of the diamondback moth (DBM), Plutella xylostella L., and it is now estimated to cost the world economy US$4–5 billion annually. Our understanding of some fundamental aspects of DBM biology and ecology, particularly host plant relationships, tritrophic interactions, and migration, has improved considerably but knowledge of other aspects, e.g., its global distribution and relative abundance, remains surprisingly limited. Biological control still focuses almost exclusively on a few species of hymenopteran parasitoids. Although these can be remarkably effective, insecticides continue to form the basis of management; their inappropriate use disrupts parasitoids and has resulted in field resistance to all available products. Improved ecological understanding and the availability of a series of highly effective selective insecticides throughout the 1990s provided ...

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Abstract: Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Abstract: Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan’s electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.