A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2. Energies 2019, 12, 3359 2 of 16users' load demand. The major concern of this scheme is the cost to be borne by users once the renewable sources cannot supply adequate power to the grid in which they will pay an expensive fossil-based electric price. Moreover, fossil energy tends to gradually increase leading to economic conflict in society [2]. Therefore, in order to tackle this issue, an appropriate demand response scheme can be applied.Demand response is the change of electric usage by users due to change of electric price or maybe an incentive as a reward of lowering their power consumption [3]. Applying demand response to this hybrid system is very beneficial for shaving peak load demand [4,5], leading to the reduction of fossil energy consumption. Moreover, it can provide short-term impact and economic benefit for both consumer and utility.In order to support this demand response, short-term load forecasting (STLF) is very important for predicting whether the energy storage from renewable sources is able to handle the forthcoming power consumption or not. If the prediction states that the storage is not adequate to support the future load, then the electricity utility can announce this situation to the users, which eventually triggers them to reduce their electric usage, because users do not only want to pay more for conventional energy source but also want to get incentives from the authorities.Fortunately, with the help of developed infrastructures like smart meters equipped with a lot of sensors and the Internet of Things (IoT), a robust STLF method is feasible to...
<span>Electric bike (E-Bike) is a bicycle driven using an electric motor and uses batteries as the energy source. It is environmentally friendly as no exhaust gas is resulted during its operation. More than one battery is normally required, being arranged in series or in parallel connection. Over limit or overloaded conditions of battery usage will reduce the lifecycle of battery, speed up its replacement and add to the maintenance cost of electric bike. This paper proposes the prevention of such degrading condition using a tool to manage the battery usage both during the charging and discharging process. The proposed electronic Battery Management System (BMS) serves to regulate, monitor, and maintain the condition of batteries to prevent any possible damage. The resulted BMS design could provide a well balancing action in a battery system consisting of 13 cells utilizing the cell-to-cell active balancing method. The test results showed that the proposed BMS could monitor the individual cell voltage with an average error of 0.032 V (0.824</span><span lang="IN">%</span><span>), while reading the charge and discharge current with an average error of 0.04 A (</span><span lang="IN">6.25%</span><span>), and the battery pack temperature with an average error of 1.21<sup>o</sup>C (</span><span lang="IN">2.9%</span><span>). Additionally, the BMS could offer a functional battery pack protection system from conditions such as undervoltage, overvoltage, overheat, and overcurrent.</span>
The circularly polarized (CP) microstrip antennas, both of singly- and doubly-fed types, possess inherent limitation in gain, impedance and axial-ratio bandwidths. These limitations are caused mainly by the natural resonance of the patch antenna which has a high unloaded Q-factor and the frequency-dependent excitation of two degenerative modes (TM01 and TM10) when using a single feed. Many applications which require circular polarization, large bandwidth, and good performance, especially in the field of wireless communication, are still difficult to be designed by using antenna software. Some consideration to take will include the application target and design specification, the materials to be used, and the method to choose (formula, numerical analysis, etc). This paper explains and analyzes the singly-fed microstrip antenna with circular polarization and large bandwidth. This singly-fed type of microstrip antenna provides certain advantage of requiring no external circular polarizer, e.g. the 900 hybrid, as it only needs to apply some perturbation or modification to a patch radiator with a standard geometry. The design of CP and large-bandwidth microstrip antenna is done gradually, by firstly truncating one tip, then truncating the whole three tips, and finally modifying it into a pentagonal patch structure and adding an air-gap to obtain larger bandwidths of impedance, gain and axial ratio. The last one antenna structure results in a novelty because it is a rare design of antenna which includes all types of bandwidth (impedance, gain, and axial ratio) being simultaneously larger than the origin antenna. The resulted characteristic performance of the 1-tip (one-tip) antenna shows respectively 1.9% of impedance bandwidth, 3.1% of gain bandwidth, and 0.45% of axial-ratio bandwidth. For the 3-tip (three-tip) step, the resulted bandwidths of respectively impedance, gain, and axial ratio are 1.7%, 3.3% and 0.5%. The pentagonal structure resulted in the bandwith values of 15.67%, 52.16% and 4.11% respectively for impedance, gain, and axial ratio.
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