Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System

Forecasting the electricity consumption is one of the most challenging tasks for energy domain stakeholders. Having reliable electricity consumption forecasts can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study regarding the forecast of electricity consumption using a methodology based on Hybrid neural Fuzzy Inference System (HyFIS). The proposed approach considers two distinct strategies, namely one strategy using only the electricity consumption as the input of the method, and the second strategy uses a combination of the electricity consumption and the environmental temperature as the input. A case study considering the forecasting of the consumption of an office building using the proposed methodologies is also presented. Results show that the second strategy is able to achieve better results, hence concluding that HyFIS is an appropriate approach to incorporate different sources of information. In this way, the environmental temperature can help the HyFIS method to achieve a more reliable forecast of the electricity consumption.The present work has been developed under the EUREKA - ITEA2 Project FUSE-IT (ITEA-13023), Project GREEDI (ANI|P2020 17822), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013