A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
Copyright policy )A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
Smart buildings seek to have a balance between energy consumption and occupant comfort. To make this possible, smart buildings need to be able to foresee sudden changes in the building’s energy consumption. With the help of forecasting models, building energy management systems, which are a fundamental part of smart buildings, know when sudden changes in the energy consumption pattern could occur. Currently, different forecasting methods use models that allow building energy management systems to forecast energy consumption. Due to this, it is increasingly necessary to have appropriate forecasting models to be able to maintain a balance between energy consumption and occupant comfort. The objective of this paper is to present an energy consumption forecasting strategy that allows hourly day-ahead predictions. The presented forecasting strategy is tested using real data from two buildings located in Valladolid, Spain. Different machine learning and deep learning models were used to analyze which could perform better with the proposed strategy. After establishing the performance of the models, a model was assembled using the mean of the prediction values of the top five models to obtain a model with better performance.
- University of Valladolid Spain
- University of Cuenca Ecuador
- Instituto Tecnológico de Santo Domingo Dominican Republic
- Instituto Tecnológico de Santo Domingo Dominican Republic
- Costa Rica Institute of Technology Costa Rica
Technology, Building Energy Efficiency and Thermal Comfort Optimization, Energy Efficiency, Energy Consumption, Operations research, Engineering, Sociology, Demand forecasting, short-term forecasting, Biology (General), Forecasting models, Forecasting models; energy consumption; multi-step forecasting; short-term forecasting; smart building, Building Energy Consumption, Energy, T, Physics, Statistics, Energy management, Engineering (General). Civil engineering (General), Social science, Smart building, FOS: Sociology, Consumption (sociology), Chemistry, Physical Sciences, TA1-2040, 3306 Ingeniería y Tecnología Eléctricas, Modelos de previsión. Consumo de energía eléctrica. Previsión a corto plazo. Edificio inteligente, QH301-705.5, Electricity Price and Load Forecasting Methods, QC1-999, smart building, Short-term forecasting, Multi-step forecasting, Ingeniería Eléctrica, energy consumption, Energy Efficiency in Manufacturing and Industry Sector, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, QD1-999, Electricity Price Forecasting, Renewable Energy, Sustainability and the Environment, multi-step forecasting, Load Forecasting, Building and Construction, Computer science, Energy consumption, Electrical engineering, Energy (signal processing), Mathematics, forecasting models
Technology, Building Energy Efficiency and Thermal Comfort Optimization, Energy Efficiency, Energy Consumption, Operations research, Engineering, Sociology, Demand forecasting, short-term forecasting, Biology (General), Forecasting models, Forecasting models; energy consumption; multi-step forecasting; short-term forecasting; smart building, Building Energy Consumption, Energy, T, Physics, Statistics, Energy management, Engineering (General). Civil engineering (General), Social science, Smart building, FOS: Sociology, Consumption (sociology), Chemistry, Physical Sciences, TA1-2040, 3306 Ingeniería y Tecnología Eléctricas, Modelos de previsión. Consumo de energía eléctrica. Previsión a corto plazo. Edificio inteligente, QH301-705.5, Electricity Price and Load Forecasting Methods, QC1-999, smart building, Short-term forecasting, Multi-step forecasting, Ingeniería Eléctrica, energy consumption, Energy Efficiency in Manufacturing and Industry Sector, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, QD1-999, Electricity Price Forecasting, Renewable Energy, Sustainability and the Environment, multi-step forecasting, Load Forecasting, Building and Construction, Computer science, Energy consumption, Electrical engineering, Energy (signal processing), Mathematics, forecasting models
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