Grey-Box Modeling for Photo-Voltaic Power Systems Using Dynamic Neural-Networks

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. TwoWell-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results

A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer

Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve. However, the satisfaction level of existing systems is not promising. New and dynamic hybrid systems are needed to imitate this mechanism. A hybrid system (a modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used to forecast students' outcomes. This proposed system would improve instruction by the faculty and enhance the students' learning experiences. The results show that a modified recurrent neural network with an adapted Grey Wolf Optimizer has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON

Systematic Digitized Treatment of Engineering Line-Diagrams

YesIn engineering design, there are many functional relationships which are difficult to express into a simple and exact mathematical formula. Instead they are documented within a form of line graphs (or plot charts or curve diagrams) in engineering handbooks or text books. Because the information in such a form cannot be used directly in the modern computer aided design (CAD) process, it is necessary to find a way to numerically represent the information. In this paper, a data processing system for numerical representation of line graphs in mechanical design is developed, which incorporates the process cycle from the initial data acquisition to the final output of required information. As well as containing the capability for curve fitting through Cubic spline and Neural network techniques, the system also adapts a novel methodology for use in this application: Grey Models. Grey theory have been used in various applications, normally involved with time-series data, and have the characteristic of being able to handle sparse data sets and data forecasting. Two case studies were then utilized to investigate the feasibility of Grey models for curve fitting. Furthermore, comparisons with the other two established techniques show that the accuracy was better than the Cubic spline function method, but slightly less accurate than the Neural network method. These results are highly encouraging and future work to fully investigate the capability of Grey theory, as well as exploiting its sparse data handling capabilities is recommended

Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced