Characterising Domestic Electricity Demand for Customer Load Profile Segmentation

The aim of this research was to characterise domestic electricity patterns of use on a diurnal, intra-daily and seasonal basis as a function of customer characteristics. This was done in order to produce a library of representative electricity demand load profiles that are characteristic of how households consume electricity. In so doing, a household’s electricity demand can be completely characterised based solely on their individual customer characteristics. A number of different approaches were investigated as to their ability to characterise domestic electricity use. A statistical regression approach was evaluated which had the advantage of identifying key dwelling, occupant and appliance characteristics that influence electricity use within the home. An autoregressive Markov chain method was applied which proved to be effective at characterising the magnitude component to electricity use within the home but failed to adequately characterise the temporal properties sufficiently. Further time series techniques were investigated: Fourier transforms, Gaussian processes, Neural networks, Fuzzy logic, and Wavelets, with the former two being evaluated fully. Each method provided disparate results but proved to be complimentary to each other in terms of their ability to characterise different patterns of electricity use. Both approaches were able to sufficiently characterise the temporal characteristics satisfactorily, however, were unable to adequately associate customer characteristics to the load profile shape. Finally clustering based approaches such as: k-means, k-medoid and Self Organising Maps (SOM) were investigated. SOM showed the greatest potential and when combined with statistical and regression techniques proved to be an effective way to completely characterise electricity use within the home and their associated customer characteristics. A library of domestic electricity demand load profiles representing common patterns of electricity use on a diurnal, intra-daily and seasonal basis within the home in Ireland and their associated household characteristics are then finally presented

A Clustering Approach to Domestic Electricity Load Profile Characterisation Using Smart Metering Data

The availability of increasing amounts of data to electricity utilities through the implementation of domestic smart metering campaigns has meant that traditional ways of analysing meter reading information such as descriptive statistics has become increasingly difficult. Key characteristic information to the data is often lost, particularly when averaging or aggregation processes are applied. Therefore, other methods of analysing data need to be used so that this information is not lost. One such method which lends itself to analysing large amounts of information is data mining. This allows for the data to be segmented before such aggregation processes are applied. Moreover, segmentation allows for dimension reduction thus enabling easier manipulation of the data. Clustering methods have been used in the electricity industry for some time. However, their use at a domestic level has been somewhat limited to date. This paper investigates three of the most widely used unsupervised clustering methods: k-means, k-medoid and Self Organising Maps (SOM). The best performing technique is then evaluated in order to segment individual households into clusters based on their pattern of electricity use across the day. The process is repeated for each day over a six month period in order to characterise the diurnal, intra-daily and seasonal variations of domestic electricity demand. Based on these results a series of Profile Classes (PC’s) are presented that represent common patterns of electricity use within the home. Finally, each PC is linked to household characteristics by applying a multi-nominal logistic regression to the data. As a result, households and the manner with which they use electricity in the home can be characterised based on individual customer attributes

Characterising Domestic Electricity Consumption Patterns by Dwelling and Occupant Socio-economic Variables: an Irish Case Study

This paper examines the influence of dwelling and occupant characteristics on domestic electricity consumption patterns by analysing data obtained from a smart metering survey of a representative cross section of approximately 4,200 domestic Irish dwellings. A multiple linear regression model was applied to four parameters: total electricity consumption, maximum demand, load factor and time of use (ToU) of maximum electricity demand for a number of different dwelling and occupant socio-economic variables. In particular, dwelling type, number of bedrooms, head of household (HoH) age, household composition, social class, water heating and cooking type all had a significant influence over total domestic electricity consumption. Maximum electricity demand was significantly influenced by household composition as well as water heating and cooking type. A strong relationship also existed between maximum demand and most household appliances but, in particular, tumble dryers, dishwashers and electric cookers had the greatest influence over this parameter. Time of use (ToU) for maximum electricity demand was found to be strongly influenced by occupant characteristics, HoH age and household composition. Younger head of households were more inclined to use electricity later in the evening than older occupants. The appliance that showed the greatest potential for shifting demand away from peak time use was the dishwasher

Secondary Re-Use of Batteries From Electric Vehicles for Building Integrated Photo-Voltaic (BIPV) applications

PV Crops is evaluating the use of battery technologies such as Vanadium Redox within Building Integrated Photovoltaic (BIPV) applications. However, their inclusion into BIPV systems will inevitably raise the overall costs of such systems. As a result, PV Crops is looking at other measures in parallel to help lower the costs associated with such systems. One particular area of interest is the potential secondary re-use of battery technology from Electric Vehicle (EV) market as a way of mitigating high costs of such systems as well as a means of encouraging battery recycling. The global installed capacity for Photovoltaic’s (PV) connected to the grid was 139 GW in 2013. This is expected to increase to an installed capacity of between 321 GW (low growth scenario) and 430 GW (high growth scenario) by 2018 [1]. Approximately two thirds of PV installations are connected to buildings, with the remainder accounting for large scale utility ground mounted systems. Like most renewable energies, BIPV generation suffers from intermittency and therefore when insufficient supply exits demand is met by importing electricity from the grid. Similarly, when supply exceeds demand surplus electricity is exported to the grid. The introduction of storage into BIPV systems negates the need for regular import/export of electricity from the grid which can lead to voltage and frequency disturbances. As a result, it allows for greater renewable energy penetration at the distribution grid by balancing supply and demand at a local level. Furthermore, integrating storage into BIPV systems can potentially assist the grid operator by facilitating demand response and frequency regulation which can help stabilise the network. The secondary re-use of batteries within the automotive industry is also being considered as a mechanism to improve the affordability of purchasing such vehicles. Currently, the battery component within Plug-in Hybrid (PEV) and Battery Electric Vehicles (BEV) represents a significant proportion of the overall capital costs. However, batteries that reach the end of their useful lifespan within the automotive industry can still be considered for other applications as between 70-80% of their original capacity still remains. In extending the useful lifespan of EV batteries for secondary applications such as BIPV, the re-sale value could potentially make EV purchase more attractive from an economic standpoint

Evaluation of Time Series Techniques to Characterise Domestic Electricity Demand

This paper discusses time series approaches, often used by Transmission System Operators (TSOs) to forecast system demand, and applies them at an individual dwelling level. In particular, two techniques, Fourier transforms and Gaussian processes were evaluated and used to characterise individual household electricity demand. The performance of the characterisation approaches were evaluated based on Pearson correlation coefficient, descriptive statistics and paired sample t-tests for electrical parameters: Total Electricity Consumption, Maximum Demand, Load Factor and Time of Use of maximum electricity demand. Finally, a number of time series tests were carried out to ensure certain properties remained between the original and characterised series. Both Fourier transforms and Gaussian processes were shown to be suitable techniques for characterising domestic electricity demand. Depending on customer demand load profiles, each approach has its own strengths and weaknesses. Fourier transforms are better at characterising the profiles of customers who consume electricity more evenly across the day (>1h). In contrast, Gaussian processes are better at characterising customers whose demand is high for only short periods of time (<1h)

Final Report: M3 Clonee – North of Kells Motorway Scheme Archaeological Services Contract 4 Navan to Kells and Kells Bypass

This is a final report of an archaeological excavation at Grange 3 which was located on the route of the M3 Navan–Kells & Kells Bypass (Archaeological Services Contract 4) of the M3 Clonee–North of Kells Motorway Scheme, County Meath. The excavation was carried out by Dr. Amanda Kelly of Irish Archaeological Consultancy Ltd on behalf of Meath County Council and the National Roads Authority. The work was carried out under Ministerial Direction No. A029/005 and National Monuments Service (NMS) Excavation Registration No. E3123 which were received from the DoEHLG in consultation with the National Museum of Ireland. The fieldwork took place between 26 June 2006 – 26 January 2007. The excavation at Grange 3 uncovered multi-period activity spanning the early Bronze Age to the early medieval period, with four major phases of activity identified. Further contemporary activity was excavated within 500m north-west and 500m south-east in the same townland. The excavated remains from this site and the other sites in Grange indicate that this area was the focus of activity over a prolonged period indicating that the locale held a certain significance ensuring its longevity in terms of human activity

Final Report: Archaeological Excavations at Grange 5, M3 Clonee North of Kells Motorway Scheme

This is a final report of an archaeological excavation at Grange 5 which was located on the route of the M3 Navan–Kells & Kells Bypass (Archaeological Services Contract 4) of the M3 Clonee–North of Kells Motorway Scheme, County Meath. The excavation was carried out by Amanda Kelly of Irish Archaeological Consultancy Ltd on behalf of Meath County Council and the National Roads Authority. The work was carried out under Ministerial Direction No. A029/003 and National Monuments Service (NMS) Excavation Registration No. E3121 which were received from the DoEHLG in consultation with the National Museum of Ireland. The fieldwork took place between 16 – 27 November 2006. A total area of 850m2 was opened around Grange 5 to reveal the archaeological features that were identified at the site during archaeological testing under licence 04E0925. Five pits, two possible postholes and two curvilinear ditches were identified at Grange 5. One of the pits was dated to the early Bronze Age but appeared to be in isolation. Two of the pits had charcoal rich fills with scorched/burnt bases and contained large quantities of charred plant remains including barley, oat and rye. A date in the Iron Age/early medieval period was established for one of these features and these have been interpreted as cereal-drying pits/features. The two curvilinear ditches were undated but respected the features outlined abov

Solar Photovoltaic Water Pumping for Multiple Use Systems (MUS) in Nepal

This paper investigates the performance of five solar photovoltaic (PV) Multiple Use Systems (MUS) used for water pumping. The solar MUS’s provide water for drinking, cleaning and micro-irrigation for some of the poorest communities in Nepal. In the absence of data logging, the performance of each system is investigated based on a series of rules of thumb to determine the predicted, expected and estimated demand and supply of water to small rural communities. The systems are compared based on their technical and economic performance and how this relates to local environmental, physical and socioeconomic characteristics at each location