RESIDENTIAL DEMAND FORECASTING METHODOLOGY FOR LONG-TERM ENERGY PLANNING IN PERU

• Autores: Meza Segura, José Neil, Luyo-Kuong, Jaime
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Nacional de Ingeniería
• Repositorio: Revistas - Universidad Nacional de Ingeniería
• Lenguaje: español
• OAI Identifier: oai:oai:revistas.uni.edu.pe:article/862
• Enlace del recurso: https://revistas.uni.edu.pe/index.php/tecnia/article/view/862
• Nivel de acceso: acceso abierto
• Materia: demanda residencial; rotación de stock; sustitución; mitigación GEI; residential demand; stock turnover; substitution; multicriteria; GHG mitigation

Globally there are demand projection models that serve as the basis for energy planning since the 1970s. However, as most of these models affected to developed countries such models must be evaluated, complemented and improved in order to identify the Methodologies that best adapt to the particularities of a developing country such as Peru and at the same time meet the challenges posed by current energy systems such as the emergence of disruptive technologies and an international context to combat climate change. The objective of this article is to define a model of projection of the demand of the residential sector by integrating the end-use models through the rotation of stocks and the substitution model through multicriteria evaluation, which was specially designed for developing countries. They have identified the factors of net present value, investment cost, presentation quality and environmental impact in the model through multicriteria evaluation so that the levels of penetration and regression by sources can be obtained and integrated into a LEAP energy model and thus evaluate the entire energy matrix as a whole. The model was applied to the case study of the residential sector in Peru and the evolution of the energy consumption equipment park was determined; the level of replacement by source and technology; as well as its comparison with the results obtained through economic models and optimization of the end use.