View Proposal

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Proposer

Sarat Dass

Title

Statistical and machine learning methods for temporal data for understanding weather variables affecting energy consumption

Goal

To study the relationship between energy consumption and weather variables using statistical and machine learning methods for temporal data.

Description

The aim of this project is discover relationships between energy consumption and weather variables using statistical models and machine learning methods for temporal data. The goal is to correlate consumption habits with weather conditions such as temperature, humidity and light. Apart of the temporal data analysis, energy measurements are also available for a group of buildings which are close to each other, which provides a measure of variability of consumption across buildings. Machine and deep learning methods are to be developed to understand all sources of variability and for making predictions. The dataset to be investigated also contains missing information whereby different imputation techniques will be investigated with their effects on predictions.

Resources

1. Francisco Monteiro, Rafael Oliveira, João Almeida, Pedro Gonçalves, Paulo Bartolomeu, Jorge Neto, Ricardo Deus, “Electricity consumption dataset of a local energy cooperative”, Data in Brief, Volume 54, 2024. 2. Jieyi Kang and David M. Reiner, “What is the effect of weather on household electricity consumption? Empirical evidence from Ireland”, Energy Economics, Volume 111, 2022. URL: https://www.sciencedirect.com/science/article/pii/S014098832200189X

Background

Url

External Link

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Sarat Dass

Keywords

machine learning, time series, multivariate analysis

Degrees

Bachelor of Science in Statistical Data Science