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Article

Load Forecasting in an Office Building with Different Data Structure and Learning Parameters

by 1,2, 1,2, 1,2 and 2,*
1
GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
2
Polytechnic of Porto, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
*
Author to whom correspondence should be addressed.
Forecasting 2021, 3(1), 242-255; https://doi.org/10.3390/forecast3010015
Received: 30 January 2021 / Revised: 16 March 2021 / Accepted: 17 March 2021 / Published: 20 March 2021
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
Energy efficiency topics have been covered by several energy management approaches in the literature, including participation in demand response programs where the consumers provide load reduction upon request or price signals. In such approaches, it is very important to know in advance the electricity consumption for the future to adequately perform the energy management. In the present paper, a load forecasting service designed for office buildings is implemented. In the building, using several available sensors, different learning parameters and structures are tested for artificial neural networks and the K-nearest neighbor algorithm. Deep focus is given to the individual period errors. In the case study, the forecasting of one week of electricity consumption is tested. It has been concluded that it is impossible to identify a single combination of learning parameters as different parts of the day have different consumption patterns. View Full-Text
Keywords: building energy management; forecast; neural network; SCADA; user comfort building energy management; forecast; neural network; SCADA; user comfort
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MDPI and ACS Style

Ramos, D.; Khorram, M.; Faria, P.; Vale, Z. Load Forecasting in an Office Building with Different Data Structure and Learning Parameters. Forecasting 2021, 3, 242-255. https://doi.org/10.3390/forecast3010015

AMA Style

Ramos D, Khorram M, Faria P, Vale Z. Load Forecasting in an Office Building with Different Data Structure and Learning Parameters. Forecasting. 2021; 3(1):242-255. https://doi.org/10.3390/forecast3010015

Chicago/Turabian Style

Ramos, Daniel, Mahsa Khorram, Pedro Faria, and Zita Vale. 2021. "Load Forecasting in an Office Building with Different Data Structure and Learning Parameters" Forecasting 3, no. 1: 242-255. https://doi.org/10.3390/forecast3010015

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