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HomeResearchProgram 1: Integrated Building SystemsRP1023: Forecasting and home energy analysis in residential energy management solutions

RP1023: Forecasting and home energy analysis in residential energy management solutions

Project leader name: 
Associate Professor Alistair Sproul
Project status: 
Current
Project period: 
06/2015 to 06/2018

The project will develop algorithms for a customer-focussed software solution that interprets energy supply and demand at the system level (focussing on residential, but applicable also to small commercial). Interpreting the complex relationship between cost, supply and load along with accurate data and analytics will enable end users to proactively manage demand. The algorithms will take local load, weather and energy generation inputs and automate the analysis of the electricity production and consumption.

Publications related to this project

Peer Reviewed Research Publications

RP1023: Conference paper: A method for classifying households to help forecasting their PV electricity self-consumption patterns

This paper analyses the impacts of household electricity load consumption profile and PV size on PV self-consumption.

RP1023: Conference paper: Household electricity load forecasting using historical smart meter data with clustering and classification techniques

Inspired by the advancements in larger scale load forecasting, this paper proposes a novel forecast method for individual household electricity loads.

RP1023: Conference Paper: Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon.

In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM)...

Peer Reviewed Research Publications

RP1023: Journal Paper: A review and analysis of regression and machine learning models on commercial building electricity load forecasting

This paper presents a review of different electricity load forecasting models with a particular focus on regression models, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models.
Peer Reviewed Research Publications

RP1023: Journal paper: A review and analysis of regression and machine learning models on commercial building electricity load forecasting

This paper presents a review of different electricity load forecasting models with a particular focus on regression models, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models. Read the paper...

CRCLCL Project Posters

Student Poster 2017: RP1023 - EVALUATION AND IMPROVEMENT OF AUSTRALIAN BUREAU OF METEOROLOGY’S SOLAR IRRADIANCE FORECASTS

Student Poster – Participants Annual Forum 2017 - Bibek Joshi EVALUATION AND IMPROVEMENT OF AUSTRALIAN BUREAU OF METEOROLOGY’S SOLAR IRRADIANCE FORECASTS
CRCLCL Project Posters

Student Poster 2017: RP1023 - FORECASTING & HOME ENERGY ANALYSIS IN RESIDENTIAL ENERGY MANAGEMENT SOLUTIONS

Student Poster – Participants Annual Forum 2017 - Baran Yildiz FORECASTING & HOME ENERGY ANALYSIS IN RESIDENTIAL ENERGY MANAGEMENT SOLUTIONS
CRCLCL Project Posters

Student poster 2016: RP1023 Forecasting & home energy analysis in residential energy management solutions

Student poster - Participants Annual Forum 2016 - Baran Yildiz Forecasting & home energy analysis in residential energy management solutions
CRCLCL Project Posters

Student Poster 2015: RP1023 Forecasting and home energy analysis in residential energy management solutions

Student Poster – Participants Annual Forum 2015 – Baran Yildiz

Residential and small commercial electricity load forecasting