Use your skills to help construct a sustainable society by predicting temperature in smart homes. Competition website
While efforts are being made around the world to minimize greenhouse gas emissions and make progress towards a more sustainable society, global energy demand continues to rise. Building energy consumption accounts for 20–40% of the total global energy consumption and Heating, Ventilation, Air Conditioning (HVAC) answer for around 50% of this amount.
Therefore, implementing energy efficiency-related strategies and optimization techniques in buildings is a critical step in reducing global energy consumption.
The University of CEU Cardenal Herrera (CEU-UCH)-Spain built a solar house to participate in the Solar Decathlon Europe 2012.
Such construction becomes a research facility that University employs in order to test innovative solutions around the area of energy efficiency. A lot of technologies have been integrated to help to reduce the overall power consumption of the house.
A predictive system based on Artificial Neural Networks (ANNs) was developed as one of them. A system like this generates a short-term forecast of indoor temperature utilizing data acquired by a complicated monitoring system as input.
The system expects to reduce the power consumption related to the Heating, Ventilation, and Air Conditioning (HVAC) systems due to the following assumptions: the high power consumption for which HVAC is responsible (53.9% of the overall consumption); and the energy needed to maintain temperature is less than the energy required to lower or increase it.
The goal is, therefore, to reduce energy consumption by predicting the indoor temperature of a room, in order to choose whether or not to activate the HVAC system. ==> These predictions could allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner.
The following is a link to the project's paper
Since 2012, time series forecasting techniques have progressed dramatically, and a significant amount of research has been undertaken in this filed, which has expanded with the advent of several cutting-edge time series prediction methods.
The objective is to predict the target value: The indoor temperature of a room (within the solar house). A variety of data science methodologies could be used (Machine Learning, Deep-learning approaches, etc.) If you're unfamiliar with Time Series, I highly recommend :
- Kaggle Time Series course. The lessons in this course are inspired by winning solutions from past Kaggle time series forecasting competitions.
If successful, you'll have put your skills to the test in a real-world time series forecasting scenario. Moreover, your model will help shed light on the general weaknesses in building heating and air-conditioning systems out there today, and shows that there is a great opportunity to reduce building energy consumption. Furthermore, your approach would serve as a wake-up call for building owners, encouraging them to evaluate their operations and start looking into new technologies. Finally, this will provide incentives for both building owners and society to collaborate even more closely in the pursuit of a more efficient and sustainable society.
I thank Dr. Francisco Zamora-Martínez, Pablo Romeu-Guallart, Dr. Juan Pardo for providing this dataset, Grupo de investigación de Sistemas Embebidos e Inteligencia Artificial (ESAI), dep. de Ciencias Físicas, Matemáticas y de la Computación, Universidad CEU Cardenal Herrerafor.