Improving load forecast accuracy by clustering consumers using smart meter data

Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in the past. This new data can be used to improve the load forecast and as a result increase the reliability and decrease the expenses of electricity providers. In this paper, a number of methods for improving load forecast using smart meter data are discussed. In these methods, consumers are first divided into a number of clusters. Then a neural network is trained for each cluster and forecasts of these networks are added together in order to form the prediction for the aggregated load. In this paper, it is demonstrated that clustering increases the forecast accuracy significantly. Criteria used for grouping consumers play an important role in this process. In this work, three different feature selection methods for clustering consumers are explained and the effect of feature extraction methods on forecast error is investigated.

Improving the quality of load forecasts using smart meter data

For the operator of a power system, having an accurate forecast of the day-ahead load is imperative in order to guaranty the reliability of supply and also to minimize generation costs and pollution. Furthermore, in a restructured power system, other parties, like utility companies, large consumers and in some cases even ordinary consumers, can benefit from a higher quality demand forecast. In this paper, the application of smart meter data for producing more accurate load forecasts has been discussed. First an ordinary neural network model is used to generate a forecast for the total load of a number of consumers. The results of this step are used as a benchmark for comparison with the forecast results of a more sophisticated method. In this new method, using wavelet decomposition and a clustering technique called interactive k-means, the consumers are divided into a number of clusters. Then for each cluster an individual neural network is trained. Consequently, by adding the outputs of all of the neural networks, a forecast for the total load is generated. A comparison between the forecast using a single model and the forecast generated by the proposed method, proves that smart meter data can be used to significantly improve the quality of load forecast.

Machine-to-machine communications with in-network data aggregation, processing, and actuation for large-scale cyber-physical systems

Machine-to-Machine (M2M) paradigm enables machines (sensors, actuators, robots, and smart meter readers) to communicate with each other with little or no human intervention. M2M is a key enabling technology for the cyber-physical systems (CPSs). This paper explores CPS beyond M2M concept and looks at futuristic applications. Our vision is CPS with distributed actuation and in-network processing. We describe few particular use cases that motivate the development of the M2M communication primitives tailored to large-scale CPS. M2M communications in literature were considered in limited extent so far. The existing work is based on small-scale M2M models and centralized solutions. Different sources discuss different primitives. Few existing decentralized solutions do not scale well. There is a need to design M2M communication primitives that will scale to thousands and trillions of M2M devices, without sacrificing solution quality. The main paradigm shift is to design localized algorithms, where CPS nodes make decisions based on local knowledge. Localized coordination and communication in networked robotics, for matching events and robots, were studied to illustrate new directions.

Smart cluster bombs - control of multi-agent systems for military applications

This paper introduces an aggregation algorithm for airborne swarming guided weapon systems, which can aggregate munitions into a given shape while reaching the surface. The algorithm uses an artificial force based controller to navigate the members of the swarm into the desired geographical position and evenly distribute them inside the shape. Inter-member repulsion forces are used to avoid collisions among members, which is crucial for a weapon deployment system. Moreover, a lower bound for the release height was obtained which guarantee convergence of the complete weapon system into the target area. The proposed swarming guided weapon system was tested using computer simulations.

RFID-based real-time smart waste management system

In an environmental context, the use of RFID (radio frequency identification) and load cell sensor technology can be employed for not only bringing down waste management costs, but also to facilitate automating and streamlining waste (e.g., garbage, recycling, and green) identification and weight measurement processes for designing smart waste management systems. In this paper, we outline a RFID and sensor model for designing a system in real-time waste management. An application of the architecture is described in the area of RFID and sensor based automatic waste identity, weight, and stolen bins identification system (WIWSBIS).

A proposed smart-card solution for Australian health services : the problems encountered

This case study describes the experience of a state government health department in evaluating the use of smart card technology to redesign health benefits programs for the disabled in Australia. The social and political context of the system is explained in detail, and the potential benefits and risks accruing to the government, health care intermediaries and the community are examined.

The reality of quality systems software : working for the machine, or smart people around the machine?

This case study-based research examines the outcomes of using quality system software (QSS) in terms of the support it provides to, and the constraints it imposes on, its users. The results indicate that QSS can help facilitate knowledge access and sharing, and to a lesser extent, facilitate communication. It can be an effective mechanism for initiating and managing changes to work processes. Although some writers have opined that information technology (IT) is another way of controlling workers, this was not evidenced in this study. Technical shortcomings did hamper performance of some tasks, and for some managers, extra work was involved.

'Don't be a smart arse': Young workers, individualization, and an ethic of enterprise in Jamie's Kitchen

In Jamie’s Kitchen the high profile celebrity chef Jamie Oliver set out to transform a group of unemployed young Londoners into the enterprising, ideal worker of 21st century flexible capitalism. The paper will argue that this reality TV series provides a means to explore key features of new work regimes. We will analyse particular aspects of the increasingly powerful individualising and normalising processes shaping the lifeworlds of young workers in a globalising risk society. Processes that require those who wish to be positively identified as entrepreneurial to do particular sorts of work on themselves; or suffer the consequences.
Drawing on Foucault’s later work on the care of the self, and the  individualization theses of the reflexive modernization literature, we identify and analyse the forms of personhood that various institutions, organisations and individuals seek to encourage in young workers; and the ways in which institutionalised risk environments increasingly individualise the risks and uncertainties associated with labour market participation. The paper argues that our understandings of what it means to be a worker of the world, are being rearticulated around the idea that we are free to choose. And we must exercise this freedom – reap its rewards, carry its obligations – as individuals.

Performance of smart antennas with receive diversity in wireless sensor networks

In this paper we propose a Geometrically Based Single Bounce Elliptical Model (GBSBEM) for multipath components involving randomly placed scatterers in the scattering region with sensors deployed on a field. The system model assumes a cluster based wireless sensor network (WSN) which collects information from the sensors, filters and modulates the data and transmit it through a wireless channel to be collected at the receiver. We first develop a GBSBE model and based on this model we develop our channel model. Use of Smart antenna system at the receiver end, which exploits various receive diversity combining techniques like Maximal Ratio Combining (MRC), Equal Gain Combining (EGC), and Selection Combining (SC), adds novelty to this system. The performance of these techniques have been proved through matlab simulations and further ahead based on different number of antenna elements present at the receiver array, we calculate the performance of our system in terms of bit-error-rate (BER). Based on the transmission power we quantify for the energy efficiency of our communication model.