In South Africa the main electricity supplier “Eskom” is struggling to meet the increasing demand. To lower the problematic electricity demand, demand side management projects are implemented by large electricity consumers. Measuring equipment is installed as part of these projects to monitor and manage the electricity consumption. Measured data is stored and can be analysed to produce information used for energy management. This, however, is a difficult and time-consuming task, because there are large volumes of data to filter through. It is repetitive work which can be automated. Various data analysis methods are available. These include plotting charts and tables using software packages or data management products. Manually analysing the data using different methods and software packages can be a long and painstaking process especially with large volumes of historical data. Information needs to be customised for different users. For example, managers need to view the end power usage and the amount of electrical energy that can be saved or was saved. Technical personnel need more detail about the electricity consumption by individual components in their system. To interpret the data in different ways a powerful tool is needed. There are many existing tools and software packages available, but most of them focus on buildings or factories. The software packages also have fixed reporting methods that are usually not customisable. In this study a customisable data analysis interface was developed to provide a solution for all the different needs. This interface is user friendly without limiting its customisable functionality. Data is received via emails, processed and then stored in a database hosted by a web server. Users access a website and configure custom charts and tables using the available data. The charts and tables are then displayed on the client’s own home page when the client logs onto the website. This interface was implemented on a website. The results of the interface were tested by automating existing reports using the customisable data analysis interface. Also when compared with the previous data analysis methods it was easily customisable, where it was very hard to customise the previous data analysis methods. It was found that the development of the customisable data analysis interface reduced man-hours spent on reporting with 70% to 80% for large energy consumers by automating the reports. The man-hours are estimated to have a value of R 20 000 to R 60 000 per month, depending on the salaries of the personnel and the volume of reports. It will help the Demand-Side Management (DSM) projects to become a continuous system to lower electricity consumption by providing information that is useful for energy management. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

In South Africa the main electricity supplier “Eskom” is struggling to meet the increasing demand. To lower the problematic electricity demand, demand side management projects are implemented by large electricity consumers. Measuring equipment is installed as part of these projects to monitor and manage the electricity consumption. Measured data is stored and can be analysed to produce information used for energy management. This, however, is a difficult and time-consuming task, because there are large volumes of data to filter through. It is repetitive work which can be automated. Various data analysis methods are available. These include plotting charts and tables using software packages or data management products. Manually analysing the data using different methods and software packages can be a long and painstaking process especially with large volumes of historical data. Information needs to be customised for different users. For example, managers need to view the end power usage and the amount of electrical energy that can be saved or was saved. Technical personnel need more detail about the electricity consumption by individual components in their system. To interpret the data in different ways a powerful tool is needed. There are many existing tools and software packages available, but most of them focus on buildings or factories. The software packages also have fixed reporting methods that are usually not customisable. In this study a customisable data analysis interface was developed to provide a solution for all the different needs. This interface is user friendly without limiting its customisable functionality. Data is received via emails, processed and then stored in a database hosted by a web server. Users access a website and configure custom charts and tables using the available data. The charts and tables are then displayed on the client’s own home page when the client logs onto the website. This interface was implemented on a website. The results of the interface were tested by automating existing reports using the customisable data analysis interface. Also when compared with the previous data analysis methods it was easily customisable, where it was very hard to customise the previous data analysis methods. It was found that the development of the customisable data analysis interface reduced man-hours spent on reporting with 70% to 80% for large energy consumers by automating the reports. The man-hours are estimated to have a value of R 20 000 to R 60 000 per month, depending on the salaries of the personnel and the volume of reports. It will help the Demand-Side Management (DSM) projects to become a continuous system to lower electricity consumption by providing information that is useful for energy management. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015