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

Daniel Ramos, Mahsa Khorram, Pedro Faria and Zita Vale
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Daniel Ramos: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
Mahsa Khorram: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
Pedro Faria: GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal
Zita Vale: Polytechnic of Porto, Rua DR, Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal

Forecasting, 2021, vol. 3, issue 1, 1-14

Abstract: 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.

Keywords: building energy management; forecast; neural network; SCADA; user comfort (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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