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

_{CRCLCL Presentations}

RP1023: Conference Presentation: Improving the accuracy of solar irradiance forecasts based on Numerical Weather Prediction using variables from multiple vertical layers as machine learning inputs

The penetration of Photovoltaic (PV) power in electricity grid is rapidly increasing. At high penetration levels, accurate solar power forecasts are indispensable in order to compensate the inherent uncertainty in solar irradiance for improving power quality and reliability, and economic integration of solar power into grid. Numerical Weather Predictions (NWPs) are physical models that predict the future state of the atmosphere on a 3-dimensional grid. Although NWP is currently the best tool for forecasting solar irradiance in few hours to several days ahead time horizon, it still exhibits substantial forecast uncertainties. Several studies have demonstrated significant improvement in solar irradiance forecasts based on NWP by applying statistical postprocessing methods using various NWP forecast variables (such as temperature, cloud cover, etc.) as machine learning inputs. However, to the authors’ knowledge, these studies are largely limited to the use of forecast variables from the surface level fields.

This presentation investigates the value of using forecast variables from multiple vertical layers of NWP as machine learning inputs in improving the accuracy of solar irradiance forecasts. Moreover, the effects of postprocessing on the NWP models with different spatio-temporal resolution – Global Forecast System (GFS), regional (ACCESS-R) and city (ACCESS-C) scale mesoscale models of the Australian Community Climate and Earth-System Simulator (ACCESS) model, are studied across different climatic locations in Australia. Functional Analysis of Variance (FANOVA) shows that the importance of NWP variables varies greatly across different climatic locations. More importantly, it is shown that in addition to the variables from surface level fields, including NWP variables from vertical layers as machine learning inputs provides further improved accuracy of solar irradiance forecasts.

Read the full presentation here:  https://bit.ly/2Pj65Jo

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

The uptake of smart grid technologies and increasing deployment of smart meters have brought greater attention on the analysis of individual household electricity consumption. Within the smart grid framework, home and battery energy management systems are becoming important demand side management tools with various benefits to households, utilities and networks.

Load forecasting is a vital component of these tools, as it can be used in optimizing the schedule of household appliances and energy operations. Inspired by the advancements in larger scale load forecasting, this paper proposes a novel forecast method for individual household electricity loads. Besides using smart meter data together with weather and temporal variables, which are commonly used in more conventional household load forecasting methods, this approach integrates the information contained in typical daily consumption profiles extracted by clustering and classification methods.

In addition to the improvements in forecast performance, the method reveals key information about a household's habitual load profiles and other important variables which impact household consumption.

Get the paper HERE.

RP1023: Journal Article: Recent advances in the analysis of residential electricity consumption and applications of smart meter data

The emergence of smart grid technologies and applications has meant there is increasing interest in utilising smart meters. Smart meter penetration has significantly increased over the last decade and they are becoming more widespread globally. Companies such as Google, Nest, Intel, General Electric and Amazon are amongst those companies which have been developing end use applications such as home and battery energy management systems which leverage smart meter data. In addition, utilities and networks are becoming more aware of the potential benefits of using household smart meter data in demand side management strategies such as energy efficiency and demand response. Motivated by this fact, the amount of research in this area has grown considerably in recent years.

This paper reviews the most recent methods and techniques for using smart meter data such as forecasting, clustering, classification and optimisation. The study covers various applications such as Home and Battery Energy Management Systems and demand response strategies enabled by the analysis of smart meter data. From a comprehensive review of the literature, it was observed that there are remarkable discrepancies between the studies, which make in-depth comparison and analysis challenging. Data analysis and reporting guidelines are suggested for studies which use smart meter data. These guidelines could provide a consistent and common framework which could enhance future research.

Read the full article here: https://doi.org/10.1016/j.apenergy.2017.10.014

_{Peer Reviewed Research Publications}

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

Smart meter data can be used for various purposes within smart grids, including residential energy applications, such as Home Energy Management Systems (HEMS) and Battery Energy Management Systems (BEMS). Considering the low feed-in tariffs for rooftop photovoltaic (PV) and increasing customer electricity prices, maximizing PV selfconsumption becomes a key objective for these energy management systems.

This paper analyses the impacts of household electricity load consumption profile and PV size on PV self-consumption. A clustering model has been developed to classify households according to their daily load and generation profiles and PV size. The study is then extended to analyse the influence of different seasons on the self-consumption forecast. The results show that the clustering model can guide HEMS and BEMS in deciding more accurate strategies for forecasting day-ahead PV self-consumption.

A method for classifying households to help forecasting their PV electricity self-consumption patterns (500009 PDF)

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

Electricity load forecasting is an important tool which can be utilized to enable effective control of commercial building electricity loads. Accurate forecasts of commercial building electricity loads can bring significant environmental and economic benefits by reducing electricity use and peak demand and the corresponding GHG emissions. 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. A comparison between the models is then presented for forecasting day ahead hourly electricity loads using real building and Campus data obtained from the Kensington Campus and Tyree Energy Technologies Building (TETB) at the University of New South Wales (UNSW). The results reveal that Artificial Neural Networks with Bayesian Regulation Backpropagation have the best overall root mean squared and mean absolute percentage error performance and almost all the models performed better predicting the overall Campus load than the single building load. The models were also tested on forecasting daily peak electricity demand. For each model, the obtained error for daily peak demand forecasts was higher than the average day ahead hourly forecasts. The regression models which were the main focus of the study performed fairly well in comparison to other more advanced machine learning models.

The article can be accessed here.