Revisão sistemática de literatura sobre modelos de previsão de consumo de energia elétrica

The growing consumption of electrical energy, climate change and the development of new technologies demand improvements for efficient energy management. An adequate forecast of the energy consumption is relevant for the sustainable development of any country. This article proposes a systematic review of selected literature based on search chains formed by the terms forecasting, energy and consumptionapplied to the scientific databases. In the article are compared mostly the models/ techniques used, the considered variables and the error metrics used for obtaining knowledge on each one of the proposals, relieve its features and thus highlight the void in the literature that might be determinant for new research work. As conclusions are made evident the continuous use of neural networks for forecasting theenergy consumption, the importance of determining the input variables and the error measuring for evaluating the precision of the models. Finally, the development of a model for the CEE short term forecast of a Latin-American developing country based on the comparison and evaluation of different  techniques/models, variables and already existing tools is proposed as a new line of research.El creciente consumo de energía eléctrica, los cambios climáticos y el desarrollo de nuevas tecnologías exigen mejoras para la gestión eficiente de la energía. El adecuado pronóstico del consumo de energía es relevante para el desarrollo sostenible de cualquier país. En este artículo se propone una revisión sistemática de literatura seleccionada a partir de cadenas de búsqueda formada por las palabras forecasting, energyy consumptionaplicadas en las bases de datos científicas. Se comparan principalmente los modelos/técnicas utilizadas, las variables consideradas y las métricas de error usadas con el fin de obtener conocimiento de cada una de las propuestas, relevar sus características y así poder evidenciar el vacío en la literatura que podría determinar la semilla para un nuevo trabajo de investigación. Como conclusiones se observan el uso continuo de redes neuronales artificiales para el pronóstico de consumo, la importancia determinar las variables de entrada y la medición del error para evaluar la precisión de los modelos. Finalmente, como nueva línea de investigación se propone desarrollar un modelo para el pronóstico de corto plazo de CEE para un país latinoamericano en vías de desarrollo, a partir de la comparación y evaluación de diferentes técnicas/modelos, variables y herramientas ya existentes.O crescente consumo de energia elétrica, as mudanças climáticas e o desenvolvimento de novas tecnologias exigem melhoras para a gestão eficiente da energia. A adequada previsão do consumo de energia é relevante para desenvolver sustentável de qualquer país. Neste artigo, é proposta uma revisão sistemática de literatura selecionada a partir de redes de busca formada pelas palavras “forecasting”, “energy” e “consumption” aplicadas nas bases de dados científicas. São comparados, principalmente, os modelos ou técnicas utilizados, as variáveis consideradas e as medidas de erro usadas a fim de obter conhecimento de cada uma das propostas, destacar suas características e, assim, poder evidenciar a lacuna na literatura quepoderia determinar a semente para um novo trabalho de pesquisa. Como conclusões, são observados o uso contínuo de redes neurais artificiais para prognosticar o consumo, a importância de determinar variáveis de entrada e a medição do erro para avaliar a exatidão dos modelos. Finalmente, como nova linha de pesquisa,propõe-se desenvolver um modelo para prever, em curto prazo, de CEE para um país latino-americano em via de desenvolvimento, a partir da comparação e da avaliação de diferentes técnicas e modelos, variáveis e ferramentas já existentes

Future Electricity Demand of the Emerging European Countries and the CIS Countries

Nowadays, one of the leading factors used in the evaluation of a country’s economic development is energy consumption. Because of economic growth, demand for energy is also increasing. In this study, the emerging European countries’ (the Czech Republic, Poland, Romania, Turkey) and the CIS countries’ (Kazakhstan, Russia, Ukraine, Uzbekistan)  electricity consumption has been forecasted for five years period (2015-2019). In the study, GM(1,1) Rolling Model, which is developed in the framework of Grey System Theory is used as a mathematical model for real-time forecasting. The results of the study show that there will not be a significant change in electricity demand in this two area during the 2015-2109 period.&nbsp

Reports to the President

A compilation of annual reports for the 1989-1990 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models

Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE)

Earth resources, a continuing bibliography with indexes

This bibliography lists 541 reports, articles and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Third International Symposium on Space Mission Operations and Ground Data Systems, part 1

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The papers focus on improvements in the efficiency, effectiveness, productivity, and quality of data acquisition, ground systems, and mission operations. New technology, techniques, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations