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© 2019 IEEE. Recommender systems assist customers to make decisions; however, the modest adoption of digital technology in the water industry means no such system exists for household water users. Such a system for the water industry would suggest to consumers the most effective ways to conserve water based on their historical data from smart water meters. The advantage for water utilities in metropolitan areas is in managing demand, such as low pressure during peak hours or water shortages during drought. For customers, effective recommendations could save them money. This paper presents a novel vision of a recommender system prototype and discusses the benefits both for the consumers and the water utility companies. The success of this type of system would depend on the ability to anticipate the time of the next major water use so as to make useful, timely recommendations. Hence, the prototype is based on a long short-term memory (LSTM) neural network that predicts significant water consumption events (i.e., showers, baths, irrigation, etc.) for 83 households. The preliminary results show that LSTM is a useful method of prediction with an average root mean square error (RMSE) of 0.403. The analysis also provides indications of the scope of further research required for developing a commercially successful recommender system.

The advent of advanced metering technologies combined with machine learning creates an opportunity to form digital multi-utility service providers. These service providers can concurrently collect a customers' water, electricity and gas demand data and provide phone and web applications to disseminate this information back to utility professionals and customers. Service companies that can provide inexpensive integrated systems will derive benefits such as operational synergies and access to mass markets not bounded by historical city, state or country limits. This article provides a vision of an integrated multi-utility service provider covering the system architecture, barriers and strategies, and opportunities and benefits. The focus of paper is to demonstrate the data modelling processes and informatics opportunities for contemporaneously collected water, electricity and gas demand data. Moreover, a transformative R&D framework is provided to realize this digital multi-utility future.

This is an ongoing research project combines System Dynamics (SD) and participatory modelling to identify variables forming the Russian Federation construction innovation system and to create an initial comprehensive conceptual model supported by stakeholder-based techniques. Quantification of this complex and dynamic construction innovation system is complicated as it includes a range of components such as information, resources, strategies, time and uncertainty. To map and conceptualize the system, system thinking and participatory techniques have been used. Methods employed academic and industry consultation and stakeholder engagement through participatory interviews. In this current paper we present the development of a conceptual model as a causal loop diagram to understand the causes, impacts and pathways leading to greater innovation performance in the Russian Federation construction industry. Moreover, the created model underpins the future development of an operationalized system dynamic model.

This work aims to facilitate higher photovoltaic (PV) penetration in distribution networks, by utilising an energy storage system (ESS) and the on-load tap-changer (OLTC) mechanism at the substation transformer. The dispatch commands to the ESS are generated by a new droop characteristic, that relates the ESS active power to the power surplus. The identification of the appropriate droop characteristic is assigned to the scheduling algorithm that generates dispatch commands both for the ESS and the OLTC. First, the ESS is dispatched alone, and then, a joint dispatch is performed combining the ESS and the OLTC. For each of the above dispatch strategies, the lowest cost solution for ESS placement and sizing (kW, kWh) is identified, and possible differences are discussed. To demonstrate the effectiveness of the proposed droop characteristic, simulation results for a real-life distribution network in Queensland are presented.

There is an opportunity for commercial customers to use energy storage to charge during low load periods and discharge during peak load periods to reduce demand charges. Energy storage control systems that incorporate load forecasts have an economic relationship with forecast error. The less the forecast error is, the more economically feasible energy storage will be. A range of time series forecast models and exponential smoothing forecast algorithms were compared to determine their applicability for use in these energy storage control systems. Model coefficients were estimated by regression and an optimization algorithm. The ARIMA model and double exponential smoothing algorithm performed the best out of the developed set of models.

This paper describes the method of a prototype forecast component of the energy resource management control algorithm for STATCOMs with battery energy storage. It is desired to be computationally efficient and of minimal complexity due to the desired purposes of forecasting each load in a LV network. The forecast model is comprised of a basis structure selected from observed electricity demand data and an electricity demand difference forecasting component estimated by the autoregressive method. The produced forecasting model had a R2 of 0.65 and a standard error of 368.55 W. During validation of the model, discrepancies between the forecasted and observed electricity demand profiles were observed. To overcome forecast model limitations, future work will involve more precise clustering of demand profiles according to additional temporal and environmental variables. This is to enable forecasts under a more diverse range of electricity demand profiles. The final developed forecasting model will be a core component of the firmware controlling STATCOMS with energy storage systems.

In this paper we present novel techniques, methods and tool flows that enable embedded systems implemented on FPGAs to start-up under tight timing constraints (i.e., hard deadlines). Meeting the application deadline is achieved by exploiting the FPGA programmability in order to implement a two-stage system start-up approach, as well as a suitable memory hierarchy. This reduces the FPGA configuration time as well as the startup time of the embedded software. Thereby the start-up time for timing-critical parts of a design neither dependent on the complexity nor on the start-up time of the complete system. An automotive case study is used to demonstrate the feasibility and quantify the benefits of the proposed approach.

Due to their their high flexibility and their increasing logic resources, FPGAs can be found in a wider application range as in recent years. But especially in application domains, where only a very restricted power budget is available, FPGAs still have to compete with other solutions. To reduce the power consumption to a minimum, many devices use different kinds of power saving modes, called sleep modes. In those modes they sacrifice functionality for the benefit of a lower consumption. Taking this idea to the extreme, many devices are only powered when it is necessary. If not, they are released from their power supply and do not drain current at all. The realization of such a sleep mode for a SRAM-based FPGA leads to difficulties. This is caused due to the fact, that the volatile memory is used to save the configuration of the device. The configuration has to be reloaded into the device every time when reattaching the power to the FPGA. This circumstance leads to restrictions for the device deployment in some electronic systems since in many cases the time a device may use to wake up is strictly limited. In several use cases, the configuration time of a SRAM based FPGA exceeds this limitation. This paper describes to decrease the configuration time of a design by exploiting the method of dynamic and partial reconfiguration in order to enable the usage of a sleep mode. With the presented method, the configuration time of any Xilinx SRAM based FPGA from the identical series (e.g. Spartan) is independent from the size of the used device.

Knowledge has been widely recognised as a determinant of business performance. Business capabilities require an effective share of resource and knowledge. Specifically, knowledge sharing (KS) between different companies and departments can improve manufacturing processes since intangible knowledge plays an enssential role in achieving competitive advantage. This paper presents a mixed method research study into the impact of KS on the effectiveness of new product development (NPD) in achieving desired business performance (BP). Firstly, an empirical study utilising moderated regression analysis was conducted to test whether and to what extent KS has leveraging power on the relationship between NPD and BP constructs and variables. Secondly, this empirically verified hypothesis was validated through explanatory case studies involving two Taiwanese manufacturing companies using a qualitative interaction term pattern matching technique. The study provides evidence that knowledge sharing and management activities are essential for deriving competitive advantage in the manufacturing industry.

Manufacturing companies have strived to enhance managerial and technical capabilities to improve business performance. Building these capabilities requires effective share of knowledge - the strategic resource. Specifically, knowledge sharing (KS) between different manufacturing departments can improve manufacturing processes since leveraging organisational knowledge plays an enssential role in achieving competitive advantage. This paper presents an empirical investigation into the impact of KS on the effectiveness of supply chain management (SCM) and the product development process (PDP) in achieving desired business performance (BP). A questionnaire survey was administered from electronic manufacturing companies operating in Taiwan. 168 valid responses were received and used to statistically examine the relationships between the concepts (SCM, PDP, KS, BP). The study findings reveal that within the Taiwanese electronic manufacturing companies KS is an essential enabler for facilitating the effectiveness of SCM and PDP in achieving desired BP.