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This paper presents electricity forecasting by integration of Nested Long Short Term Memory (NLSTM) as the another type of LSTM, Attention Mechanism (AM) as the technique for Neural Network and optimized by Cuckoo Search Optimizer (CSO) as the metaheuristic. It is important because of the electricity forecasting issue. Electricity is the crucial energy, due to the many sectors which is require it. Estimation of electricity consumption can enhance the effectiveness of generation and supply. The shortage and outage of electricity is the thing that should be avoided through the electricity forecasting. In order to create better forecasting result, the forecasting by NLSTM-AM will be combined. NLSTM-AM has several parameters which influence the network performance. Those parameters will be optimized by CSO. To discover the performance of this proposed method, the RMSE of this method will be compared by other algorithms. In this paper there are two datasets which will be used, those are Taiwan electricity consumption and Turkey electricity consumption. It is proved that NLSTM-AM-CSO has the smallest RMSE rather than other algorithms. [ABSTRACT FROM AUTHOR]

Information systems and technology are expected to have a good impact on the civil registration service process including during the COVID-19 pandemic. DISPENDUKCAPIL Surabaya had transformed the civil registration service systems which are LAMPID system into KLAMPID, and KLAMPID COVID system later during the COVID-19 pandemic by implementing the information system. These transformations are proven in reducing the paper utilization and transportation activities which are expected to reduce the environmental impact that is generated by the civil registration service activities. However, those transformations are also proven to increase electricity consumption and internet use since there is information system equipment such as computers and databases equipment which are utilized. This study tries to evaluate the total environmental impact in each transformation by using the Life Cycle Assessment (LCA) approach. The environmental impact calculation results from SimaPro software for LAMPID, KLAMPID, KLAMPID COVID offline submission, and KLAMPID COVID online submission are 24.64 mPt, 41.95 mPt, 41.71 mPt, and 36.81 mPt respectively. This study indicates that the information system implementation was able to reduce the environmental impact from the paper utilization and transportation activities reduction, however, those reductions cannot cover the environmental impact that is generated from the information system equipment. [ABSTRACT FROM AUTHOR]

The building industry accounts for the largest percentage of the total energy use and carbon emissions globally. The per capita electricity consumption in the United Arab Emirates (UAE) is one of the highest in the world, of which the built environment accounts for 70% of energy consumption, as compared to the global average of 40%, mainly due to the need for cooling. The residential sector as the dominant infrastructure component in a city carries a heavy energy usage for cooling to mitigate the local extreme hot climate. A condition further exacerbated by the lack of an energy building code until 2010. Hence, this paper explores the opportunities for building energy efficiency retrofitting in an existing representative residential unit in Al Ain city, UAE. First, thermal leakage through the building envelope was audited through infrared thermography to identify the impact of time, workmanship and construction quality on the building thermal efficiency. Then, building envelope upgrades were tested through energy simulation. The objective is to identify energy upgrade opportunities and prioritize retrofitting solutions. Residential units in two housing complexes built over a decade apart were selected for testing in Al Ain city, UAE. Thermal testing at critical points including corner wall junctions, roof junction, windows frames and external glazed sliding doors was carried out. The auditing highlighted some common thermal behavior and variances, including a critical need for building envelope insulation as expected. Newer units, however, had significant thermal anomalies around building's junctions, indicating that building's age may not be the main referent for a retrofitting priority. Workmanship and construction quality may be a more critical factor. The older unit that showed less thermal leakage compared to the newer unit has been selected for energy simulation to test the building envelope upgrades. The results indicate potential annual electricity savings up to 48.6% through building envelope insulation leading to a 50% reduction of CO2 emissions. [ABSTRACT FROM AUTHOR]

The increasing electricity demand in Freetown, capital of Sierra Leone. has been a burden on the country's electricity sector, since the electricity demanded by residents in Freetown outweighs the electricity generated by the nation's Electricity Generation and Transmission Company (EGTC). This problem has resulted to power shortage leaving many areas within the capital city for hours or days without electricity supply. A major challenge faced by the country's electricity generating sector is the losses on the distribution and transmission networks, which accounts for 34.5% losses in the capital of Freetown alone according to 2017 government electricity report. A 161 kilovolts (kV) of the national transmission network runs from a 250km Bumbuna hydroelectric power plant to the Freetown substations. Another source of electricity loss in Freetown, according to the Electricity Distribution and Supply Authority (EDSA), is non-technical losses resulting from human theft and illegal connections from the national grid by its residents. This paper assesses the development of an efficient energy audit program to minimise electricity loses within Freetown. This study involves the use of a quantitative data collection approach using randomly distributed questionnaires to residents within Freetown. From the data collected and analysed, shows that the development of an effective and efficient energy audit program within Freetown can minimize energy wastage in buildings as well as electricity losses. [ABSTRACT FROM AUTHOR]

Carbone dioxide emissions is a global issue which has catastrophic consequences that beyond the universal climate change. Carbon emissions interact with other human demands on this planet, such as food, fibers, timber, and land for dwellings and roads. This paper presents a linear programming model for the optimal electricity generation-mix problem to meet a specified CO2 emission target. The objective function minimizes the weighted sum of two terms: the electricity generation cost and the CO2 emissions cost. Most of the electricity generation mix problem literature considers fixed and aggregated demand and capacity over the year. However, the electricity demand and generation capacity are dynamic parameters in hourly and/or daily basis. The proposed model contributes to the literature by modeling the hourly electricity demand, daily and hourly electricity generation capacity, and daily CO2 emissions limit. The model is solved optimally with a case study derived based on the electricity sector 2030 plan in South Africa. Results show that the proposed model proposes generation-mix plans that could achieve the CO2 reduction target. Furthermore, electricity generation cost and CO2 emissions are eliminated. [ABSTRACT FROM AUTHOR]

This paper is an attempt to analyze the relation of energy consumption with socio-economic and building geometric features of residential buildings in Dhaka city. Energy consumption data are collected from households' monthly electricity bill charged by Dhaka Electricity Supply Company; while socio-economic characteristics and building features are obtained through household interview and measurement of buildings' floor and window dimensions. The study finds that, energy consumption has a strong positive relation with number of appliances, monthly income, flat size and window area. Pearson correlation results indicate that, influences of socioeconomic characteristics on energy consumption are more noteworthy than building design criteria. Opening and thermal characteristics of window materials also affect buildings' energy consumption. [ABSTRACT FROM AUTHOR]

Electricity as the energy form of final energy consumption is necessary for functioning of economy and society in general. It is an input in creating added value for goods and services in national economies. The correlation between electricity consumption and gross domestic product, as the most significant macroeconomic variable, is strong. Electricity indisputably has a positive impact on a nation's standard and its growth as it is necessary for functioning of the life of individuals and families in their households with regard to their technological equipment. The importance of electricity is also seen in exceptional, extraordinary circumstances where significant, sudden "disturbances" in functioning of societies and countries in general occur. One of such extraordinary disturbances is an occurrence of various pandemics. The latest one appeared in the first quarter of 2020 in almost all countries in the world. Croatia was no exception, and the pandemic has impacted its functioning. For the first time in the most recent history of the world all countries were brought to a state of isolation from other countries in the world and a shutdown of almost the entire economic activity i.e. the lockdown. The COVID-19 pandemic broke out in China in December 2019, and its world-wide spread, with the exponential growth in the number of infected and death cases as pandemic consequences, has impacted a significant decrease in economic activity as well as changes in people's habits. Decreasing in population activities and "shutting down" economies has resulted in a significant impact on all goods and service markets, both on the side of supply and demand. The impact has spread onto energy markets, crude oil and electricity market, where the demand has decreased significantly. The decrease in electricity demand is different from country to country. In addition, significant changes in load characteristics with respect to consumption categories, household and enterprise, have occurred. The paper analyses the stated changes in Croatia in the first four months of 2020 in relation to a three-year average, from 2017 to 2019. In addition, changes in a daily electricity demand in selected EU countries which have also been pandemic-hit are analysed. Significant changes in daily diagrams of household and enterprise consumption with minor changes in total electricity consumption have been noticed. [ABSTRACT FROM AUTHOR]

The smart grid is a new technology using innovative and sophisticated methods of electrical transmission and distribution in order to provide excellent electrical services to customers. It is a two-way communication system that allows users to manage their energy service, as well as access smart grid convenience features, such as using energy when it is at low cost, reading current consumption electricity bills online, scheduling turning on/off home appliances, and managing alternative energy sources. These conveniences, however, introduce risks to the smart grid system, increase the possibility of cyberattacks, and cascade failures propagating from one system to another. The security of the smart grid is very critical. The intend of this paper is to survey the attempts that have been made to tackle smart grid security, and to present the security approaches necessary to enforce tough security measures that fully protect the smart grid infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, our previous work of Load-Based Power Saving (LBPS) for energy saving at the user side is extended to support integrated sleep scheduling for BS, RS, and MSS in the IEEE 802.16j Multi-Hop Relay Network. Topology-dependent time frame structure is adopted in our design to reduce the transmission delay in the relay network, and two LBPS schemes, namely LBPS-Aggr-MR and LBPS-Merge-MR, are proposed. Simulation study shows the proposed LBPS schemes significantly outperform the standard Type I PSC in terms of power saving efficiency. [ABSTRACT FROM AUTHOR]

Due to limited battery capacity, electronics in biomedical devices require low power consumption. On the other hand, biomedical devices that integrate multiple functions like sensing, amplification, signal conditioning, signal processing, data storage, etc. have put a greater constraint on power consumption for each functional unit. This paper presents a low power design of a decimator for a sigma-delta ADC for biomedical applications in a commercial 0.18µm CMOS process. Two features help make the design low power: use of bit-serial architecture and use of an ultra-low supply voltage of 0.9V in a 0.18 µm CMOS design. The design interfaces with a sigma-delta modulator with a clock rate of 1MHz. [ABSTRACT FROM AUTHOR]

Tool condition monitoring systems (TCMS) have approached detection of tool wear over time from several perspectives: visual inspection, vibrational analysis, acoustic emission, power consumption and sound analysis. Current research focuses on monitoring methods involving expensive and complex sensor systems placed in close proximity to the machining operation itself. Understandably, industry has been slow to adopt these methods due to the initial high cost of sensors and the risk of damage to those sensors in the industrial environment. Methods that can monitor industrial machining processes from more remote locations would allow protection of valuable sensor systems and involve simpler installations. Researchers have established a significant relationship between tool wear and the sound signature of machine tools, yet there has been little work in developing this into a workable application. Issues encountered by researchers focus mainly on separating background noise from the noise of the tool itself, and methods to automate the recognition of tool wear conditions in sound signatures. This paper reviews the current state of research in the sound monitoring of tool condition in machine shops. [ABSTRACT FROM AUTHOR]

Electricity demand forecast plays an important role in the energy section. The future demand for electricity is the basis for the respective electricity production planning, distribution and development mechanism, crucial for economic development in a country. Electricity usage is a rapidly grown phenomenon in developing region, increasingly complex to forecast. Artificial neural networks (ANN) have been applied to many complex and high-dimensional forecasting problems. The inference of ANN concerns the imitation of the human brain functioning, emerged as a great performance tool to obtain classification, pattern recognition and complex forecasts. Layer recurrent neural network (LRNN) is a dynamic neural network uses a feedback and time delay element, the output is based on the current input and the pervious input and output. This paper considers dynamic neural network model that implement LRNN principles to forecast household Electricity demand. LRNN is a sequential network rather than a combinational, made the networks fault-tolerant. [ABSTRACT FROM AUTHOR]

This paper presents an UWB low noise amplifier (LNA) based on current reused topology to achieve low power on overall circuit in the band of 3~16GHz. Measurement of the input and output reflection coefficient S11, S22 are less than -10 dB, the maximum amplifier gain S21 gives 9.7dB, the minimum of the noise figure is 4.2dB, the measured IIP3 is -8.5dBm at 6GHz. It consumes 11mW power consumption from 1.1-V supply voltage. This chip was implemented by TSMC 0.18µm 1P6M process. [ABSTRACT FROM AUTHOR]

A transition to an electrified transportation system is widely assumed to be an important step along the road to environmental sustainability. However, large penetrations of electric vehicles (EVs) may put electricity grids under critical strain, since peaks in electricity demand are likely to increase radically. Efforts to manage demand peaks through pricing schemes may create new peaks at low-price periods, if large numbers of EV owners try to benefit from low prices. We propose a pricing method, called consumer utility driven pricing, which learns from EV owners' reactions to sub-optimal prices and adjusts announced prices accordingly. We evaluate our results in simulations, where we find that consumer utility driven pricing outperforms current electricity pricing schemes. We test our method in matching both flat and extremely volatile demand profiles and we see that in both cases it performs closely to what it is theoretically possible under perfect information. [ABSTRACT FROM AUTHOR]