Optimal sampling plan for clean development mechanism lighting projects with lamp population decay

This paper proposes a metering cost minimisation model that minimises metering cost under the constraints of sampling accuracy requirement for clean development mechanism (CDM) energy efficiency (EE) lighting project. Usually small scale (SSC) CDM EE lighting projects expect a crediting period of 10 years given that the lighting population will decay as time goes by. The SSC CDM sampling guideline requires that the monitored key parameters for the carbon emission reduction quantification must satisfy the sampling accuracy of 90% confidence and 10% precision, known as the 90/10 criterion. For the existing registered CDM lighting projects, sample sizes are either decided by professional judgment or by rule-of-thumb without considering any optimisation. Lighting samples are randomly selected and their energy consumptions are monitored continuously by power meters. In this study, the sampling size determination problem is formulated as a metering cost minimisation model by incorporating a linear lighting decay model as given by the CDM guideline AMS-II.J. The 90/10 criterion is formulated as constraints to the metering cost minimisation problem. Optimal solutions to the problem minimise the metering cost whilst satisfying the 90/10 criterion for each reporting period. The proposed metering cost minimisation model is applicable to other CDM lighting projects with different population decay characteristics as well

Optimal metering plan for measurement and verification on a lighting case study

M&V (Measurement and Verification) has become an indispensable process in various incentive EEDSM (energy efficiency and demand side management) programmes to accurately and reliably measure and verify the project performance in terms of energy and/or cost savings. Due to the uncertain nature of the unmeasurable savings, there is an inherent trade-off between the M&V accuracy and M&V cost. In order to achieve the required M&V accuracy cost-effectively, we propose a combined spatial and longitudinal MCM (metering cost minimisation) model to assist the design of optimal M&V metering plans, which minimises the metering cost whilst satisfying the required measurement and sampling accuracy of M&V. The objective function of the proposed MCM model is the M&V metering cost that covers the procurement, installation and maintenance of the metering system whereas the M&V accuracy requirements are formulated as the constraints. Optimal solutions to the proposed MCM model offer useful information in designing the optimal M&V metering plan. The advantages of the proposed MCM model are demonstrated by a case study of an EE lighting retrofit project and the model is widely applicable to other M&V lighting projects with different population sizes and sampling accuracy requirements.http://www.journals.elsevier.com/energy2017-01-31hb2016Electrical, Electronic and Computer Engineerin

Improvements to longitudinal Clean Development Mechanism sampling designs for lighting retrofit projects

An improved model for reducing the cost of long-term monitoring in Clean Development Mechanism (CDM) lighting retrofit projects is proposed. Cost-effective longitudinal sampling designs use the minimum numbers of meters required to report yearly savings at the 90% confidence and 10% relative precision level for duration of the project (up to 10 years) as stipulated by the CDM. Improvements to the existing model include a new non-linear Compact Fluorescent Lamp population decay model based on the Polish Efficient Lighting Project, and a cumulative sampling function modified to weight samples exponentially by recency. An economic model altering the cost function to a net present value calculation is also incorporated. The search space for such sampling models is investigated and found to be discontinuous and stepped, requiring a heuristic for optimisation; in this case the Genetic Algorithm was used. Assuming an exponential smoothing rate of 0.25, an inflation rate of 6.44%, and an interest rate of 10%, results show that sampling should be more evenly distributed over the study duration than is currently considered optimal, and that the proposed improvements in model accuracy increase monitoring costs by 21.4% in present value terms.Centre for New Energy Systems and the National Hub for the Postgraduate Programme in Energy Efficiency and Demand Side Management at the University of Pretoria.http://www.elsevier.com/locate/apenergyhb201

Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central Universities and the Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin

A review for solar panel fire accident prevention in large-scale PV applications

Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas

Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting : a comparative analysis of Grad-CAM and SHAP

DATA AVAILABILITY: Datasets related to this article can be found at [63], an open-source online data repository hosted at Mendeley Data.This study investigates the efficacy of Explainable Artificial Intelligence (XAI) methods, specifically Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP), in the feature selection process for national demand forecasting. Utilising a multi-headed Convolutional Neural Network (CNN), both XAI methods exhibit capabilities in enhancing forecasting accuracy and model efficiency by identifying and eliminating irrelevant features. Comparative analysis revealed Grad-CAM’s exceptional computational efficiency in high-dimensional applications and SHAP’s superior ability in revealing features that degrade forecast accuracy. However, limitations are found in both methods, with Grad-CAM including features that decrease model stability, and SHAP inaccurately ranking significant features. Future research should focus on refining these XAI methods to overcome these limitations and further probe into other XAI methods’ applicability within the time-series forecasting domain. This study underscores the potential of XAI in improving load forecasting, which can contribute significantly to the development of more interpretative, accurate and efficient forecasting models.National Key R&D Program of China, National Natural Science Foundation of China, National Research Foundation China/South Africa Research Cooperation Programme, China/South Africa Bilateral, and Royal Academy of Engineering Transforming Systems through Partnership.http://www.elsevier.com/locate/apenergyElectrical, Electronic and Computer Engineerin

A real-time energy management and speed controller for an electric vehicle powered by a hybrid energy storage system

A real-time unified speed control and power flow management system for an electric vehicle (EV) powered by a battery-supercapacitor hybrid energy storage system (HESS) is developed following a nonlinear control system technique. In view of the coupling between energy management and HESS sizing, a HESS sizing model is developed in this article to optimally determine the size of HESS to serve an EV using the controller designed. The objectives of the controller are to track the set speed of the vehicle with globally exponential stability and to make use of the HESS wisely to reduce battery stress. The design provides a compound controller by exploiting the physical origins of the vehicles' power demand. The controller and HESS sizing system designed are simulated on a standard urban dynamometer driving schedule and a recorded actual city driving cycle for a full-size EV to demonstrate their effectiveness.The National Research Foundation and the University of Pretoria.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424hj2020Electrical, Electronic and Computer Engineerin

An experimental study on defrosting performance for an air source heat pump unit with a horizontally installed multi-circuit outdoor coil

When frost forms and accumulates over the outdoor coil’s surface in an air source heat pump (ASHP) unit, system operating performance will be dramatically deteriorated. Reverse cycle defrosting is the most widely used standard defrosting method. A previous related study reported that downwards flowing of melted frost due to gravity over a vertical multi-circuit outdoor coil would decrease the reverse cycle defrosting performance. If the outdoor coil can be changed to horizontally installed, the flow path of melted frost over coil surface can be shortened, and the flow directions of refrigerant and melted frost changed from opposite to orthogonal. Consequently, a better defrosting performance is expected. In this paper, therefore, an experimental study on defrosting performance for an ASHP unit with a horizontally installed multi-circuit outdoor coil was conducted. Experimental results show that, when a vertical outdoor coil was changed to horizontally installed, the defrosting efficiency increased 9.8%, however, with the same defrosting duration at 186 s. Furthermore, when the outdoor air fan was reversed to blowing the melted frost during defrosting, the total mass of the retained water collected decreased 222 g. However, the defrosting efficiency was not increased, but decreased 6.6% because of the heat transfer enhanced between hot coil and cold ambient air.The Hong Kong Polytechnic University, and the Guangdong University of Technology.http://www.elsevier.com/locate/apenergy2017-03-31hb2016Electrical, Electronic and Computer Engineerin

Optimal maintenance planning for sustainable energy efficiency lighting retrofit projects by a control system approach

The energy savings achieved by implementing energy efficiency (EE) lighting retrofit projects are sometimes not sustainable and vanish rapidly given that lamp population decays as time goes by if without proper maintenance activities. Scope of maintenance activities refers to replacements of failed lamps due to nonrepairable lamp burnouts. Full replacements of all the failed lamps during each maintenance interval contribute to a tight project budget due to the expense for the lamp failure inspections, as well as the procurement and installation of new lamps. Since neither “no maintenance” nor “full maintenance” is preferable to the EE lighting project developers (PDs), we propose to design an optimal maintenance plan that optimises the number of replacements of the failed lamps, such that the EE lighting project achieves sustainable performance in terms of energy savings whereas the PDs obtain their maximum benefits in the sense of cost–benefit ratio. This optimal maintenance planning (OMP) problem is aptly formulated as an optimal control problem under control system framework, and solved by a model predictive control (MPC) approach. An optimal maintenance plan for an EE lighting retrofit project is designed as a case study to illustrate the effectiveness of the proposed control system approach.A preliminary version of this paper has been presented in the 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, 24–29 August 2014.http://www.elsevier.com/locate/conengprachj201