Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Global solar irradiation prediction using a multi-gene genetic programming approach

This is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this record.In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by contemporary researchers. The method proposed here results in nonlinear analytical expressions, unlike those with neural networks which is essentially a black box modelling approach. This additional flexibility is an advantage from the modelling perspective and helps to discern the important variables which affect the prediction. Due to the evolutionary nature of the algorithm, it is able to get out of local minima and converge to a global optimum unlike the back-propagation (BP) algorithm used for training neural networks. This results in a better percentage fit than the ones obtained using neural networks by contemporary researchers. Also a hold-out cross validation is done on the obtained genetic programming (GP) results which show that the results generalize well to new data and do not over-fit the training samples. The multi-gene GP results are compared with those, obtained using its single-gene version and also the same with four classical regression models in order to show the effectiveness of the adopted approach

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model

The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model

An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context

California is making major strides towards meeting its greenhouse gas emission reduction goals with the transformation of its electrical grid to accommodate renewable generation, aggressive promotion of building energy efficiency, and increased emphasis on moving toward electrification of end uses (e.g., residential heating, etc.). As a result of this activity, the State is faced with significant challenges of systemwide resource adequacy, power quality and grid reliability that could be addressed in part with demand responsive (DR) load modifying strategies using controllable building technologies. Dynamic facades have the ability to potentially shift and shed loads at critical times of the day in combination with daylighting and HVAC controls. This study explores the technical potential of dynamic facades to support net load shape objectives. A model predictive controller (MPC) was designed based on reduced order thermal (Modelica) and window (Radiance) models. Using an automated workflow (involving JModelica.org and MPCPy), these models were converted and differentiated to formulate a non-linear optimization problem. A gradient-based, non-linear programming problem solver (IPOPT) was used to derive an optimal control strategy, then a post-optimization step was used to convert the solution to a discrete state for facade actuation. Continuous state modulation of the façade was also modeled. The performance of the MPC controller with and without activation of thermal mass was evaluated in a south-facing perimeter office zone with a three-zone electrochromic window for a clear sunny week during summer and winter periods in Oakland and Burbank, California. MPC strategies reduced total energy cost by 9–28% and critical coincident peak demand was reduced by up to 0.58 W/ft2-floor or 19–43% in the 4.6 m (15 ft) deep south zone on sunny summer days in Oakland compared to state-of-the-art heuristic control. Similar savings were achieved for the hotter, Burbank climate in Southern California. This outcome supports the argument that MPC control of dynamic facades can provide significant electricity cost reductions and net load management capabilities of benefit to both the building owner and evolving electrical grid