ALBERT

Description: Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective management of power systems. The electricity demand and prices, however, exhibit specific features, including non-constant mean and variance, calendar effects, multiple periodicities, high volatility, jumps, and so on, which complicate the forecasting problem. In this work, we compare different modeling techniques able to capture the specific dynamics of the demand time series. To this end, the electricity demand time series is divided into two major components: deterministic and stochastic. Both components are estimated using different regression and time series methods with parametric and nonparametric estimation techniques. Specifically, we use linear regression-based models (local polynomial regression models based on different types of kernel functions; tri-cubic, Gaussian, and Epanechnikov), spline function-based models (smoothing splines, regression splines), and traditional time series models (autoregressive moving average, nonparametric autoregressive, and vector autoregressive). Within the deterministic part, special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors. This work considers electricity demand data from the Nordic electricity market for the period covering 1 January 2013–31 December 2016. To assess the one-day-ahead out-of-sample forecasting accuracy, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are calculated. The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand. Further, vector autoregressive modeling combined with spline function-based regression gives superior performance compared with the rest.

Description: In this paper, the discrete phase model (DPM) was introduced to study the fuel injector cavitations process and the macro spray characteristic of three different types of nozzle spray shape with diesel and hybrid biofuel blend for several injection pressures and backpressures. The three types of nozzle spray shapes used were circle, elliptical A type, and elliptical B type. The cavitations’ flows inside the injector nozzles were simulated with Computer Fluid Dynamics (CFD) simulations using the cavitations mixture approach. The effect of nozzle spray shape towards the spray characteristic of hybrid biofuel blends is analyzed and compared with the standard diesel. Furthermore, a verification and validation from both the experimental results and numerical results are also presented. The nozzle flow simulation results indicated that the fuel type did not affect the cavitation area vastly, but were more dependent on the nozzle spray shape. In addition, the spray width of the elliptical nozzle shape was higher as compared to the circular spray. Moreover, as the backpressure increased, the spray width downstream increased as well. The spray tip penetration for the elliptical nozzle shape was shorter than the circular nozzle shape due to circular nozzles having smaller nozzle widths and lesser spray cone angles. Thus, this resulted in smaller aerodynamic drag.

Description: The effect of iron-doped cerium oxide (FeCeO2) nanoparticles as a fuel additive was experimentally investigated with waste cooking oil methyl ester (WCOME) in a four-stroke, single cylinder, direct injection diesel engine. The study aimed at the reduction of harmful emissions of diesel engines including oxides of nitrogen (NOx) and soot. Two types of nanoparticles were used: cerium oxide doped with 10% iron and cerium oxide doped with 20% iron, to further investigate the influence of the doping level on the nanoparticle activity. The nanoparticles were dispersed in the tested fuels at a dosage of 90 ppm with the aid of an ultrasonic homogenizer. Tests were conducted at a constant engine speed of 2000 rpm and varying loads (from 0 to 12 N.m) with neat diesel (D100) and biodiesel–diesel blends of 30% WCOME and 70% diesel by volume (B30). The engine combustion, performance, and emission characteristics for the fuel blends with nanoparticles were compared with neat diesel as the base fuel. The test results showed improvement in the peak cylinder pressure by approximately 3.5% with addition of nanoparticles to the fuel. A reduction in NOx emissions by up to 15.7% were recorded, while there was no noticeable change in unburned hydrocarbon (HC) emissions. Carbon monoxide (CO) emission was reduced by up to 24.6% for B30 and 15.4% for B30 with nano-additives. Better engine performance was recorded for B30 with 20% FeCeO2 as compared to 10% FeCeO2, in regard to cylinder pressure and emissions. The brake specific fuel consumption was lower for the fuel blend of B30 with 10% FeCeO2 nanoparticles, in low-to-medium loads and comparable to D100 at high loads. Hence, a higher brake thermal efficiency was recorded for the blend in low-to-medium loads compared to D100.

Description: Microbial fuel cell (MFC) technology offers an alternative means for producing energy from waste products. In this review, several characteristics of MFC technology that make it revolutionary will be highlighted. First, a brief history presents how bioelectrochemical systems have advanced, ultimately describing the development of microbial fuel cells. Second, the focus is shifted to the attributes that enable MFCs to work efficiently. Next, follows the design of various MFC systems in use including their components and how they are assembled, along with an explanation of how they work. Finally, microbial fuel cell designs and types of main configurations used are presented along with the scalability of the technology for proper application. The present review shows importance of design and elements to reduce energy loss for scaling up the MFC system including the type of electrode, shape of the single reactor, electrical connection method, stack direction, and modulation. These aspects precede making economically applicable large-scale MFCs (over 1 m3 scale) a reality.

Description: Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of 〈 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Description: Energy management technology of demand-side is a key process of the smart grid that helps achieve a more efficient use of generation assets by reducing the energy demand of users during peak loads. In the context of a smart grid and smart metering, this paper proposes a hybrid model of energy disaggregation through deep feature learning for non-intrusive load monitoring to classify home appliances based on the information of main meters. In addition, a deep neural model of supervised energy disaggregation with a high accuracy for giving awareness to end users and generating detailed feedback from demand-side with no need for expensive smart outlet sensors was introduced. A new functional API model of deep learning (DL) based on energy disaggregation was designed by combining a one-dimensional convolutional neural network and recurrent neural network (1D CNN-RNN). The proposed model was trained on Google Colab’s Tesla graphics processing unit (GPU) using Keras. The residential energy disaggregation dataset was used for real households and was implemented in Tensorflow backend. Three different disaggregation methods were compared, namely the convolutional neural network, 1D CNN-RNN, and long short-term memory. The results showed that energy can be disaggregated from the metrics very accurately using the proposed 1D CNN-RNN model. Finally, as a work in progress, we introduced the DL on the Edge for Fog Computing non-intrusive load monitoring (NILM) on a low-cost embedded board using a state-of-the-art inference library called uTensor that can support any Mbed enabled board with no need for the DL API of web services and internet connectivity.

Description: Designing a suitable controller for air-conditioning systems to reduce energy consumption and simultaneously meet the requirements of the system is very challenging. Important factors such as stability and performance of the designed controllers should be investigated to ensure the effectiveness of these controllers. In this article, the stability and performance of the fuzzy cognitive map (FCM) controller are investigated. The FCM method is used to control the direct expansion air conditioning system (DX A/C). The FCM controller has the ability to do online learning, and can achieve fast convergence thanks to its simple mathematical computation. The stability analysis of the controller was conducted using both fuzzy bidirectional associative memories (FBAMs) and the Lyapunov function. The performances of the controller were tested based on its ability for reference tracking and disturbance rejection. On the basis of the stability analysis using FBAMS and Lyapunov functions, the system is globally stable. The controller is able to track the set point faithfully, maintaining the temperature and humidity at the desired value. In order to simulate the disturbances, heat and moisture load changed to measure the ability of the controller to reject the disturbance. The results showed that the proposed controller can track the set point and has a good ability for disturbance rejection, making it an effective controller to be employed in the DX A/C system and suitable for a nonlinear robust control system.

Description: One of the most notable challenges in wireless communications is energy scarcity, which has attracted considerable attention in Fifth Generation (5G) wireless network research. This paper investigates the performance of energy harvesting (EH) relays under the best relay selection (BRS) scheme. The results show degradation of spectral efficiency (SE) due to EH relaying compared with conventional cooperative relaying (CR). Conversely, EH relaying provides a positive gain compared with conventional CR, increasing the lifetime of the network and decreasing energy consumption (EC) and operational cost. Moreover, the EH relaying network has better energy efficiency (EE) compared with conventional relaying networks. Results show that when EH relaying is applied, EE is improved through an increased number of relays. Finally, the SE-EE metric is presented for both conventional and EH relays. Results show that the performance of the proposed technique was able to achieve a maximum SE of 1.4 bits/s/Hz and maximum EE at 0.6 bits/s/Hz, and for the case of conventional relays, a maximum SE of 2 bits/s/Hz and EE at 1.1 bits/s/Hz. This result implies that the proposed EH scheme provides an optimum solution for energy-constrained wireless CR systems.

Description: This paper presents a hardware testbed for testing the building energy management system (BEMS) based-on the multi agent system (MAS). The objective of BEMS is to maximize user comfort while minimizing the energy extracted from the grid. The proposed system implements a multi-objective optimization technique using a genetic algorithm (GA) and the fuzzy logic controller (FLC) to control the room temperature and illumination setpoints. The agents are implemented on the low cost embedded systems equipped with the WiFi communication for communicating between the agents. The photovoltaic (PV)-battery system, the air conditioning system, the lighting system, and the electrical loads are modeled and simulated on the embedded hardware. The popular communication protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus TCP/IP are adopted for integrating the proposed MAS with the existing infrastructures and devices. The experimental results show that the sampling time of the proposed system is 16.50 s. Therefore it is suitable for implementing the BEMS in a real-time where the data are updated in an hourly or minutely basis. Further, the proposed optimization technique shows better results in optimizing the comfort index and the energy extracted from the grid compared to the existing methods.