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Journal ArticleDOI

Improving building energy performance by addressing today's demand-side challenges: A review of contributions from Latin America

TL;DR: The lista procesada resulto en un total de 73 estudios as discussed by the authors, a total of 176 documentos como lista inicial, and most of which focused on the aspectos relacionados con los ocupantes, soluciones pasivas, de bajo consumo, and tecnicas de prevision for edificios inteligentes.
Abstract: Debido a la actual crisis energetica mundial y como resultado de los acuerdos de las naciones participantes, se han establecido medidas por las Naciones Unidas para superar los desafios relacionados. A pesar de los esfuerzos para incluir a los paises subdesarrollados en dicho proceso de decision, la mayoria de las contribuciones continuan estando inclinadas al hemisferio norte. Asi, este trabajo se enfoca en destacar los esfuerzos realizados por los paises Latinoamericanos (LA), entre 2018-2020, para contribuir especificamente en las mejoras en el desempeno energetico en edificaciones para abordar los desafios actuales del lado de la demanda. Dichos desafios estan relacionados con la gestion de la demanda: (i) picos de demanda no controlados y (ii) capacidad de transmision y distribucion insuficiente en la red electrica. Las contribuciones de LA se clasifican en independientes, colaboracion y aplicacion. Los estudios tambien se clasificaron en teoricos, experimentales, ambos y revisiones. La metodologia de filtrado de dos etapas implementada dio como resultado un total de 176 documentos como lista inicial. Al centrarse solo en los aspectos relacionados con los ocupantes, las soluciones pasivas y de bajo consumo y las tecnicas de prevision para edificios inteligentes, la lista procesada resulto en un total de 73 estudios. Los resultados mostraron que los esfuerzos realizados por los paises LA residen en su mayoria en la implementacion de estrategias previamente desarrolladas y propuestas por paises desarrollados, para realizar estudios de caso como independiente o en colaboracion. Finalmente, se presenta un analisis de fortalezas, oportunidades, debilidades y amenazas (FODA) para explicar los resultados obtenidos.

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Journal ArticleDOI
08 Sep 2021-Energies
TL;DR: The basis of the minimum energy efficiency and the renewable threshold for the definition of ZEB is established in order to better understand the application in Panama, based on assessing the energy regulations implemented in Panama.
Abstract: In recent decades, European countries have developed concepts, definitions, and construction technologies for Zero Energy Building (ZEB) that are effective and correspond to their specific climates. Latin American countries are still trying to find adequate solutions which respond to the local climatic, cultural, social, technical, and economic context. As such, this paper aims to establish the basis of the minimum energy efficiency and the renewable threshold for the definition of ZEB in order to better understand the application in Panama, based on assessing the energy regulations implemented in Panama. To achieve this aim, a review concentrated on the concept-definition and implementation adopted by Latin American countries is presented first before the paper converges into defining a framework for application in Panama. Finally, a case-study-based theoretical framework proposing a ZEB definition for Panama is discussed. The results of this study showed a net primary energy balance, of which the range falls into a plus energy building definition, indicating that all of the cases studied could supply their electricity needs using Photovoltaic generation. All dwellings studied have the potential to become a plus energy building, depending on the available roof surface area. Finally, a strengths, weaknesses, opportunities and threats analysis is presented in order to assess and support the introduction of such a ZEB definition and framework.

5 citations

References
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01 Jan 2000
TL;DR: In this article, a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets is presented.
Abstract: This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side.

436 citations

Journal ArticleDOI
01 Feb 2000
TL;DR: This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets.
Abstract: This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side.

402 citations

Journal ArticleDOI
TL;DR: An overview of AI methods utilised for DR applications is provided, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects, where AI methods have been used for energy DR.
Abstract: Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.

251 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the influence of occupant behaviour on energy consumption for space heating and found that the number of usage hours for the heating system has a stronger effect on energy usage than temperature setting.
Abstract: What are the key determinants and effects of occupants' behaviour on energy use for space heating? Statistical analyses were carried out on energy use and self-reported behaviour data from a household survey in the Netherlands. Results showed that the number of usage hours for the heating system have a stronger effect on energy consumption than temperature setting. Small correlations were found between energy use and the ventilation system, since most households barely use the ventilation system. The main building characteristic determining behaviour is the type of temperature control. Households with a programmable thermostat were more likely to keep the radiators turned on for more hours than households with a manual thermostat or manual valves on radiators. In relation to household characteristics, the presence of elderly persons in the household proved to be a determining factor in the use of the heating system and ventilation. As a result of wide variations in preferences and lifestyle, occupant behaviour has emerged as an important contributor to energy use in dwellings. The results indicate that the type of heating and ventilation system has an influence on occupant behaviour.

240 citations

Journal ArticleDOI
TL;DR: In this paper, the authors tried to find answers to two main questions for forecasting loads for individual consumers: First, can current short term load forecasting (STLF) models work efficiently for forecasting individual households? Second, do the anthropologic and structural variables enhance the forecasting accuracy of individual consumer loads?

186 citations