Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

A Software Tool for Optimal Sizing of PV Systems in Malaysia

This paper presents a MATLAB based user friendly software tool called as PV.MY for optimal sizing of photovoltaic (PV) systems. The software has the capabilities of predicting the metrological variables such as solar energy, ambient temperature and wind speed using artificial neural network (ANN), optimizes the PV module/ array tilt angle, optimizes the inverter size and calculate optimal capacities of PV array, battery, wind turbine and diesel generator in hybrid PV systems. The ANN based model for metrological prediction uses four meteorological variables, namely, sun shine ratio, day number and location coordinates. As for PV system sizing, iterative methods are used for determining the optimal sizing of three types of PV systems, which are standalone PV system, hybrid PV/wind system and hybrid PV/diesel generator system. The loss of load probability (LLP) technique is used for optimization in which the energy sources capacities are the variables to be optimized considering very low LLP. As for determining the optimal PV panels tilt angle and inverter size, the Liu and Jordan model for solar energy incident on a tilt surface is used in optimizing the monthly tilt angle, while a model for inverter efficiency curve is used in the optimization of inverter size

Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth

Smart microgrids are a possibility to reduce complexity by performing local optimization of power production, consumption and storage. We do not envision smart microgrids to be island solutions but rather to be integrated into a larger network of microgrids that form the future energy grid. Operating and controlling a smart microgrid involves optimization for using locally generated energy and to provide feedback to the user when and how to use devices. This chapter shows how these issues can be addressed starting with measuring and modeling energy consumption patterns by collecting an energy consumption dataset at device level. The open dataset allows to extract typical usage patterns and subsequently to model test scenarios for energy management algorithms. Section 3 discusses means for analyzing measured data and for providing detailed feedback about energy consumption to increase customers’ energy awareness. Section 4 shows how renewable energy sources can be integrated in a smart microgrid and how energy production can be accurately predicted. Section 5 introduces a self-organizing local energy system that autonomously coordinates production and consumption via an agent-based energy auction system. The final section discusses how the proposed methods contribute to sustainable growth and gives an outlook to future research

A New Approach for Optimal Sizing of Standalone Photovoltaic Systems

This paper presents a new method for determining the optimal sizing of standalone photovoltaic (PV) system in terms of optimal sizing of PV array and battery storage. A standalone PV system energy flow is first analysed, and the MATLAB fitting tool is used to fit the resultant sizing curves in order to derive general formulas for optimal sizing of PV array and battery. In deriving the formulas for optimal sizing of PV array and battery, the data considered are based on five sites in Malaysia, which are Kuala Lumpur, Johor Bharu, Ipoh, Kuching, and Alor Setar. Based on the results of the designed example for a PV system installed in Kuala Lumpur, the proposed method gives satisfactory optimal sizing results

An Improved Method for Sizing Standalone Photovoltaic Systems Using Generalized Regression Neural Network

In this research an improved approach for sizing standalone PV system (SAPV) is presented. This work is an improved work developed previously by the authors. The previous work is based on the analytical method which faced some concerns regarding the difficulty of finding the model’s coefficients. Therefore, the proposed approach in this research is based on a combination of an analytical method and a machine learning approach for a generalized artificial neural network (GRNN). The GRNN assists to predict the optimal size of a PV system using the geographical coordinates of the targeted site instead of using mathematical formulas. Employing the GRNN facilitates the use of a previously developed method by the authors and avoids some of its drawbacks. The approach has been tested using data from five Malaysian sites. According to the results, the proposed method can be efficiently used for SAPV sizing whereas the proposed GRNN based model predicts the sizing curves of the PV system accurately with a prediction error of 0.6%. Moreover, hourly meteorological and load demand data are used in this research in order to consider the uncertainty of the solar energy and the load demand

A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that the proposed model has better prediction accuracy compared to some empirical and statistical models. Two error statistics are used in this research to evaluate the proposed model, namely, mean absolute percentage error and root mean square error. These values for the proposed model are 11.8% and −3.1%, respectively. Finally, the proposed model shows better ability in overcoming the sophistic nature of the solar radiation data

On the Performance of Hybrid PV/Unitized Regenerative Fuel Cell System in the Tropics

Solar hydrogen system is a unique power system that can meet the power requirements for future energy demands. Such a system uses the hydrogen as the energy carrier, which produces energy through the electrolyzer with assistance of the power from the PV during the sunny hours, and then uses stored hydrogen to produce energy through the fuel cell after sunset or on cloudy days. The current study has used premanufactured unitized regenerative fuel cells in which the electrolyzer and the fuel cell function within one cell at different modes. The system components were modeled and the one-day real operational and simulated data has been presented and compared. The measured results showed the ability of the system to meet the proposed load, and the total efficiency was about 4.5%

A comparative study of three types of grid connected photovoltaic systems based on actual performance

In this study, three photovoltaic (PV) systems are evaluated based on actual performance. The energy generation of three types of PV systems namely concentrating PV system (6 units × 1 kWp), PV system with sun tracking flat (2 units × 1 kWp) and fixed flat PV system (2 units × 1 kWp) is analyzed in this research. Data analysis for ten consecutive months consisting of 12,190 samples of 15 min interval is done. The performance evaluation is done using energy yield, yield factor, capacity factor, power efficiency and PV array efficiency. Based on the experiment data, it is concluded that tracking flat PV system is the most suitable system for Malaysia in normal operation mode with average daily generation of 4.7 kW h (141 kW h as a monthly average), system efficiency of 11%, power efficiency of 85%, average daily yield factor of 2.3 kW h/kWp and capacity factor of 32%. This study also highlights the PV energy (EPV) models for each PV generators with respect to the environmental factors. The advantage of employing a tracking flat system as compared to the fixed flat system is considered based on the effectiveness of the dual-axis tracking mechanism tracking the sun for maximum power output

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Seeing revolution non-linearly: www.filmingrevolution.org

Filming Revolution, launched in 2015, is an online interactive data base documentary tracing the strands and strains of independent (mostly) documentary filmmaking in Egypt since the revolution. Consisting of edited interviews with 30 filmmakers, archivists, activists, and artists based in Egypt, the website is organised by the themes that emerged from the material, allowing the viewer to engage in an unlimited set of “curated dialogues” about issues related to filmmaking in Egypt since 2011. With its constellatory interactive design, Filming Revolution creates as much as documents a community of makers, as it attempts to grapple with approaches to filmmaking in the wake of such momentous historical events. The non-hierarchical polysemous structure of the project is meant to echo the rhizomatic, open-ended aspect of the revolution and its aftermath, in yet another affirmation and instantiation of contemporary civil revolution as a non-linear, ever-unfolding, on-going, event