RT info:eu-repo/semantics/article
T1 Comparative study of continuous hourly energy consumption forecasting strategies with small data sets to support demand management decisions in buildings
A1 Mariano‐Hernández, D.
A1 Hernández Callejo, Luis
A1 Solís, M.
A1 Zorita‐Lamadrid, A.
A1 Duque‐Pérez, O.
A1 Gonzalez‐Morales, L.
A1 Alonso‐Gómez, V.
A1 Jaramillo‐Duque, A.
A1 Santos García, F.
K1 Building energy consumption
K1 Forecasting
K1 Learning algorithms
K1 Multistep forecasting
K1 Short‐term forecasting
AB Buildings are one of the largest consumers of electrical energy, making it important to develop different strategies to help to reduce electricity consumption. Building energy consumption forecasting strategies are widely used to support demand management decisions, but these strategies require large data sets to achieve an accurate electric consumption forecast, so they are not commonly used for buildings with a short history of record keeping. Based on this, the objective of this study is to determine, through continuous hourly electricity consumption forecasting strategies, the amount of data needed to achieve an accurate forecast. The proposed forecasting strategies were evaluated with Random Forest, eXtreme Gradient Boost, Convolutional Neural Network, and Temporal Convolutional Network algorithms using 4 years of electricity consumption data from two buildings located on the campus of the University of Valladolid. For performance evaluation, two scenarios were proposed for each of the proposed forecasting strategies. The results showed that for forecasting horizons of 1 week, it was possible to obtain a mean absolute percentage error (MAPE) below 7% for Building 1 and a MAPE below 10% for Building 2 with 6 months of data, while for a forecast horizon of 1 month, it was possible to obtain a MAPE below 10% for Building 1 and below 11% for Building 2 with 10 months of data. However, if the distribution of the data captured in the buildings does not undergo sudden changes, the decision tree algorithms obtain better results. However, if there are sudden changes, deep learning algorithms are a better choice.
PB Wiley
SN 2050-0505
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/67809
UL https://uvadoc.uva.es/handle/10324/67809
LA eng
NO Energy Science & Engineering, december 2022, 10(12), 4694-4707
NO Producción Científica
DS UVaDOC
RD 18-nov-2024