Complex energy simlulation using simplified user interaction mechanisms

Simulation of energy systems and associated thermodynamic domains is very powerful in delivering precise information at high resolution. Modelling software requires detailed information about the energy system. The specialised user usually has questions about specific aspects of the energy system and may not be interested in the complete set of outputs available from simulation results. Similarly the specialised user may only be concerned about a subset of the inputs provided to the software. This suggests an opportunity to develop an input / output scheme tailored for the specialised user. The power of simulation can be accessed through the use of simplified interfaces. Although these restrict flexibility in terms of model input / output data the specialised user is only interested in a subset of the capability of the underlying simulation tool. Robust results rely on a consistent underlying simulation context, this restricted interface ensures that only the parameters of interest to the users are modifiable and that other simulation parameters remain fixed ensuring a consistent and repeatable output. One such example of limited user interaction for both output and input is the ADEPT interface to whole building and plant dynamic modelling and simulation suite ESP­r (ESRU 2002). The interface was developed in the context of the UK domesticheating market. This paper describes the development of the ADEPT tool and associated spreadsheet templates in order to provide a readily usable platform for the study of domestic heating systems and controls for plant and control components manufacturers, regulatory authorities and research organisations

Development of concepts for a zero-fossil-energy greenhouse

Dutch government and greenhouse horticultural practice aim for strongly reduced fossil energy use and of environmental loads in 2010 and energy neutral greenhouses in 2020. This research aims to design a greenhouse concept with minimal use of fossil energy and independent of nearby greenhouses. The concept is called the zero-fossil-energy-greenhouse. This paper presents a theoretical design study and analysis to assess the viability of a zero-fossil-energy-greenhouse concept. The greenhouse was designed for Dutch circumstances and relies on available state-of-art technologies. Nine concepts were generated and evaluated by a panel of experts. Although, none of the concepts was unanimously selected, one of the concepts received on-average highest votes. It uses an aquifer for long term heat and cold storage. Geothermal heat and a heat pump connected to the warm pit of the aquifer are used to heat of the greenhouse. Electricity need is covered by green-electricity. Cooling and dehumidification of the greenhouse is realised by a heat pump combined with the cold aquifer pit. This concept was more thoroughly evaluated in a simulation study that assessed design consistency and evaluated greenhouse performance in view of design requirements. From the simulations it was concluded that a combination of geothermal heat and a heat pump/aquifer can cover the heat demand of the greenhouse with help of heat buffers, but a fully closed greenhouse concept is not manageable in the summer season. With given technology the chosen concept was not able to cool and dehumidify greenhouse air to target temperature and humidity. A semi closed greenhouse solves this problem

Municipal Energy Management: Best Practices from DVRPC's Direct Technical Assistance Program

This guide highlights best practices and lessons learned from municipal energy management projects in southeastern Pennsylvania. In 2013 and 2014, DVRPC worked with nine municipalities in southeastern Pennsylvania to provide direct technical assistance with measuring, analyzing, and developing implementation strategies for energy management in municipal buildings. The goal of energy management is to identify opportunities for improving how energy is being used at a facility and to develop analyses that support decision making on how best to prioritize and implement these improvements. These improvements can remedy various problems -- high energy and maintenance costs due to malfunctioning, poorly installed or aging equipment, poor occupant comfort due to a lack of weatherization, or poorly controlled equipment. This guide will illustrate several best practices for identifying and implementing energy management opportunities that save money and improve building comfort

Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant

The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring better product quality, and generally increasing profits. This article describes a decision system developed for optimizing the energy efficiency of a petrochemical plant. The system has been developed after a data mining process of the parameters registered in the past. The designed system carries out an optimization process of the energy efficiency of the plant based on a combined algorithm that uses the following for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in the past and, on the other hand, a module of two neural networks to obtain new interpolated operation points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to select the parameters most important in the plant and to study the behavior of the energy efficiency index. This study also helped ensure an optimal training of the neural networks. The robustness of the system as well as its satisfactory results in the testing process (an average rise in the energy efficiency of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to implement and to integrate the decision system as a pilot software in an SCADA

Exploring the role of messenger effects and feedback frames in promoting uptake of energy-efficient technologies

The persuasive potential for varying messenger types and feedback frames to increase pro-environmental choice was explored in a 2 (feedback frame: financial vs. environmental) × 5 (messenger type: neighbour, government, industry, utilities vs. control) factorial design experiment. Using the context of home heating choice, 493 non-student participants were given information on either the financial or environmental benefits of selecting an energy-efficient heat pump versus a standard boiler, as described by one of four messenger types (versus a no-messenger control). Likelihood of selecting the ‘green’ technology was assessed, as well as any carry-over effects on real-life behavioural intentions. Additionally, we assessed the messenger attributes that appeared to be most important in this context, in terms of whether sources that were perceived to be trustworthy, knowledgeable, or a combination of both dimensions, would hold greater sway over preference formation. Overall, no evidence was found for any impact of messenger type on either preference formation or behavioural intentions. However, message content (i.e. how information on the benefits of pro-environmental choice was framed), was found to have substantial impact on behaviour; with the financial versus environmental decision frame being significantly more likely to encourage uptake of the energy-efficient versus standard technology. We suggest that the level of processing required for the kinds of large-scale purchase decisions we consider here may explain the lack of any messenger effect on choice behaviour. Implications for the development of behaviour change interventions designed to promote consideration of energy-efficient technologies in this context are discussed

Comparing the Online Learning Capabilities of Gaussian ARTMAP and Fuzzy ARTMAP for Building Energy Management Systems

Recently, there has been a growing interest in the application of Fuzzy ARTMAP for use in building energy management systems or EMS. However, a number of papers have indicated that there are important weaknesses to the Fuzzy ARTMAP approach, such as sensitivity to noisy data and category proliferation. Gaussian ARTMAP was developed to help overcome these weaknesses, raising the question of whether Gaussian ARTMAP could be a more effective approach for building energy management systems? This paper aims to answer this question. In particular, our results show that Gaussian ARTMAP not only has the capability to address the weaknesses of Fuzzy ARTMAP but, by doing this, provides better and more efficient EMS controls with online learning capabilities

Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants

An effective modeling technique is proposed for determining baseline energy consumption in the industry. A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the current consumption and production in the event that no energy-saving measures had been implemented. Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable accuracy levels of prediction are detected, confirming good capability of the models for predicting plant behavior and their suitability for baseline energy consumption determining purposes. High level of robustness is observed for ANN against uncertainty affecting measured values of variables used as input in the models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive industry. Application of ANN technique would also help to overcome the limited availability of on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes

Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Part of this work has been developed from results obtained during the H2020 “Optimised Energy Efficient Design Platform for Refurbishment at District Level” (OptEEmAL) project, Grant No. 680676