Modelling Energy Demand Response Using Long-Short Term Memory Neural Networks

We propose a method for detecting and forecasting events of high energy demand, which are managed at the national level in demand side response programmes, such as the UK Triads. The methodology consists of two stages: load forecasting with long short-term memory neural network and dynamic filtering of the potential highest electricity demand peaks by using the exponential moving average. The methodology is validated on real data of a UK building management system case study. We demonstrate successful forecasts of Triad events with RRMSE ≈ 2.2% and MAPE ≈ 1.6% and general applicability of the methodology for demand side response programme management, with reduction of energy consumption and indirect carbon emissions

The incorporation of virtual ergonomics to improve the occupational safety condition in a factory

Proper manual material handling (MMH) is the important step leading to the occupational safety of the workers on the shop floor as well as the productivity improvement of the manufacturing process. The objectives of this study are the application of different risk assessment methods, the redesign of the workstation to reduce the occupational risk and the utilization of software package to validate the proposed interventions. As a result, an assembly line of a product is selected as the case study to validate the proposed agenda. Afterwards, four lifting assessment methods, i.e. NIOSH lift equation, Snook Psychophysical Table, OSU Lift guidelines and ACGIH/TLV, are used to assess the hazard risk in the assembly line. After these methods are performed, the results are introduced to recommend the newly designed working conditions, i.e. postures, movements and the barriers. To validate the improved design, new configurations are simulated by the virtual ergonomic program and the ergonomic analysis is performed. The important results, e.g. low back compression and percent of population capable, are calculated by the software to determine the appropriate values which are used as the guidelines for a safe working condition. Moreover, the manufacturing process is also simulated to improve that the ergonomic redesign of the shop floor environment and another consequence of the implementation leads to the significant increase of the productivity

Optimization of fused filament fabrication system by response surface method

Fused filament fabrication (FFF) is a 3D printing or additive manufacturing method used for rapid prototyping and manufacturing. The characterization and optimization of process parameters in FFF is of critical importance because the quality of the specimens produced by this method substantially depends on the appropriate setting of various significant factors. In this study, the FFF printing process using acrylonitrile butadiene styrene (ABS) as the filament material was investigated for the optimization of significant factors in the process. Three potential factors, namely nozzle temperature, bed temperature, and printing speed, were included in this study as the inputs, while surface roughness of the specimens was considered as the output. Roughness measurements were made on the flat surfaces at the top and bottom of the specimens. As the ranges for optimal factor settings were recommended by the manufacturer, the Box-Behnken design, which is a response surface method (RSM), was utilized in this study. In each treatment, two replicas of the test specimens were used for the confirmation test. The results of the statistical analyses indicated that the bed temperature and the printing speed had a significant impact on the surface roughness. Another finding was that there was a non-linear relationship between the bed temperature and the surface roughness. The optimal settings for the factors arrived at in this study can serve as guidelines for the practitioners to achieve the highest performance when they use FFF with ABS filaments

The Application of System identification method to characterize the performance of NiMH batteries in hybrid vehicles

Nickel-metal hydride (Ni-MH) battery is one of the electric sources which is widely used in hybrid electric vehicles. As a result, it is important to understand the characteristics of Ni-MH battery which is connected to direct current machine in the vehicle. However, the crucial problem is the complexity of the vehicle system which deals with the charging and discharging process of battery in order to maintain the designated speed. The system is considered as a black box and the system identification method is utilized in to characterize the dynamic behavior of the system. The system inputs are battery voltage, armature current and state of charge (SOC) while the output is the speed of DC machine. However, the system identification method will not work properly if the available data available has played an importable role on the determination of the model. As a result, the data regarding all parameters were collected and transmitted to the data logger and used to construct different models. The results from the system identification method indicate that the autoregressive model with exogenous input (ARX) is the most appropriate model to explain the relationship between inputs and output. Therefore, the performance of hybrid vehicle related to the characteristics of Ni-MH batteries is elaborately characterized and this study leads to the effective maneuver

Surface roughness prediction of FFF-fabricated workpieces by artificial neural network and Box–Behnken method

Fused Filament Fabrication (FFF) or Fused Deposition Modelling (FDM) or three-dimension (3D) printing are rapid prototyping processes for workpieces. There are many factors which have a significant effect on surface quality, including bed temperature, printing speed, and layer thickness. This empirical study was conducted to determine the relationship between the above-mentioned factors and average surface roughness (Ra). Workpieces of cylindrical shape were fabricated by an FFF system with a Polylactic acid (PLA) filament. The surface roughness was measured at five different positions on the bottom and top surface. A response surface (Box-Behnken) method was utilised to design the experiment and statistically predict the response. The total number of treatments was sixteen, while five measurements (Ra1, Ra2, Ra3, Ra4 and Ra5) were carried out for each treatment. The settings of each factor were as follows: bed temperature (80, 85, and 90 °C), printing speed (40, 80 and 120 mm/s), and layer thickness (0.10, 0.25 and 0.40 mm). The prediction equation of surface roughness was then derived from the analysis. The same set of data was also used as the inputs for a machine learning method, an artificial neural network (ANN), to construct the prediction equation of surface roughness. Rectified linear unit (ReLU) was utilised as the activation function of ANN. Two training algorithms (resilient backpropagation with weight backtracking and globally convergent resilient backpropagation) were applied to train multi-layer perceptrons. Moreover, the different number of neurons in each hidden layer was also studied and compared. Another interesting aspect of this study is that the ANN was based on a limited number of training samples. Finally, the prediction errors of each method were compared, to benchmark the prediction performance of the two methods: Box-Behnken and ANN

Surface Roughness Reduction in A Fused Filament Fabrication (FFF) Process using Central Composite Design Method

The objective of this study is to optimize the fabrication factors of a consumer-grade fused filament fabrication (FFF) system. The input factors were nozzle temperature, bed temperature, printing speed, and layer thickness. The optimization aims to minimize average surface roughness (Ra) indicating the surface quality of benchmarks. In this study, Ra was measured at two positions, the bottom and top surface of benchmarks. For the fabrication, the material used was the Polylactic acid (PLA) filament. A response surface method (RSM), central composite design (CCD), was utilized to carry out the optimization. The analysis of variance (ANOVA) was calculated to explore the significant factors, interactions, quadratic effect, and lack of fit, while the regression analysis was performed to determine the prediction equation of Ra. The model adequacy checking was conducted to check whether the residual assumption still held. The total number of thirty benchmarks was fabricated and measured using a surface roughness tester. For the bottom surface, the analysis results indicated that there was the main effect from only one factor, printing speed. However, for the top surface, the ANOVA signified an interaction between the printing speed and layer thickness. The optimal setting of these factors was also recommended, while the empirical models of Ra at both surface positions were also presented. Finally, an extra benchmark was fabricated to validate the empirical model

A Comparison of Various Forecasting Methods for Autocorrelated Time Series

The accuracy of forecasts significantly affects the overall performance of a whole supply chain system. Sometimes, the nature of consumer products might cause difficulties in forecasting for the future demands because of its complicated structure. In this study, two machine learning methods, artificial neural network (ANN) and support vector machine (SVM), and a traditional approach, the autoregressive integrated moving average (ARIMA) model, were utilized to predict the demand for consumer products. The training data used were the actual demand of six different products from a consumer product company in Thailand. Initially, each set of data was analysed using Ljung-Box-Q statistics to test for autocorrelation. Afterwards, each method was applied to different sets of data. The results indicated that the SVM method had a better forecast quality (in terms of MAPE) than ANN and ARIMA in every category of products