Daily Electricity Consumption Research Articles

Hybrid solar power plant for lighting power source in the politeknik negeri jember engineering department building

The sun is one of the potential renewable energy sources in Indonesia. One use of solar energy is to generate electricity. The Politeknik Negeri Jember Engineering Building already has a hybrid type solar power plant. However, it has not been utilized optimally because it is not connected to the building’s electrical network. This research aims to plan the connection of a solar power plant with an engineering building lighting system. The electrical network for the Engineering Department building that will be supplied by the solar power plant is the electric lighting system on the 3rd floor. The cable chosen is 4 mm2 with a cable length of 81 meters. Electrical connection from the solar power plant to the engineering building electrical network is carried out via an automatic transfer switch. If the condition of the electricity generated is adequate, the electricity will be connected automatically to the building’s lighting electricity network. If the electrical energy produced is insufficient or has run out, the lighting electricity network will automatically connect to the main electricity source. The building’s solar power plant is equipped with an internet of things system to monitoring its performance. The daily lighting electricity consumption is around 2.55 kwh.

Preface

IOP Conference Series: Materials Science and Engineering International Nuclear Science, Technology and Engineering Conference 2023 (iNuSTEC2023) “Nuclear Science and Technology For Socio-economic Development “ Universiti Tenaga Nasional Kajang, Selangor, Malaysia 10-12 October 2023 MESSAGE FROM: VICE-CHANCELLOR, UNIVERSITI TENAGA NASIONAL (UNITEN)UNITEN is honored to host the International Nuclear Science, Technology, and Engineering Conference 2023 (iNUSTEC2023) and the Nuclear Youth Competition (NYC). With the theme “Nuclear Science and Technology for Sustainable Development Goals”, iNuSTEC2023 is the thirteenth in a series of conferences hosted by the Malaysian Nuclear Society (MNS) since 2010 through collaboration with research institutes and local universities.Don’t we feel like time has passed by so quickly? And with it, came along significant and some were unthinkable changes to our lives. According to Our World in Data statistics, in 2022, each of us consumed on average 3.5 times more energy compared to 40 years ago. In the year 2050, Malaysia is expected to consume almost twice the amount of electricity in 2022 (320 TWh vs. 180 TWh). This means that on a daily basis in 2050, Malaysia is expected to consume close to 0.9 TWh of electricity per day.About 50 million electric vehicles are expected to be on the road in 2050. Every day, about 0.6 TWh of electricity is needed to charge these cars. That is an additional two-thirds of the expected daily electricity consumption per person in 2050. Malaysia has identified the pathways for Responsible Transition 2050 in the National Energy Transition Roadmap (NETR) to tackle the energy trilemma of affordability, security, and sustainability. These pathways include having 0% coal share and 70% renewable energy share of installed capacity in 2050.70% of renewable energy share is about 57 GW of solar PV, which will take a total land size that is slightly smaller than Melaka state. The same amount of power can be generated by nuclear power on a total land size that is 10 times smaller. That is not even considering the intermittency of power generation with solar PV.At UNITEN, we believe that research and knowledge in nuclear power can play a huge role in supporting the Low Carbon Nation Aspiration and the Responsible Transition, especially in providing affordable, clean and safe energy supply to the Rakyat. Institute of Nuclear Energy (INE) at UNITEN was established in May this year to further empower and accelerate the existing research and educational efforts done at UNITEN since 2010.At the university level, UNITEN has established the National Energy Centre (NEC) to support the national energy transition efforts. NEC will serve as a hub for research institutes, industrial and international linkages, and government agencies to form collaborations with experts from local and foreign universities, energy agencies, and stakeholders.UNITEN envisions that NEC will play a significant role in providing technological solutions for Malaysia’s energy transition which will be beneficial to Malaysia, as well as the surrounding region. NEC will also play the role of capacity building for the country’s future workforce that will lead Malaysia towards net-zero in 2050. I extend my warmest invitation to every participant attending this conference, to be a part of this concerted nationwide effort in championing sustainability through involvement with the NEC.I would like to remind participants, that you are among the very small percentage of Malaysia’s population, who are experts in the nuclear field. Thank you so much for your contributions to keeping the knowledge related to nuclear science and engineering alive, despite how hard or negative it can get in the industry.Congratulations to the organizing committee for hosting this iNuSTEC2023 together with the Nuclear Youth Competition (NYC2023). My deepest appreciation goes to our keynote speakers, technical co-sponsors, and all supporters for their contribution to ensuring the success of this conference. To the presenters and participants, thank you for your participation and I wish the conference all success. PROFESSOR IR. DR. NOOR AZUAN ABU OSMAN Vice-Chancellor Universiti Tenaga Nasional List of Editors, Contents, Papers, iNuSTEC2023 Organising Committee, Nuclear Youth Competition 2023 Secretariat, Nuclear Youth Competition 2023, Acknowledgement, Mns Council Members 2023/2025, are available in the pdf.

Farm electricity system simulator (FESS): A platform for simulating electricity utilisation on dairy farms

The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS’s utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs.

Classification model of electricity consumption behavior based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering

The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China.

A Transfer Learning-based Method for the Daily Electricity Consumption Forecasting of Large Industrial Users after Business Expansion

Background: With the rapid development of industry, the expansion capacity and frequency of large industrial users continue to increase. However, the traditional static prediction model is difficult to accurately predict the daily electricity consumption of industrial expansion, which is not conducive to the safe and stable operation of the power grid. Objective: In response to the above problems, this paper proposes a transfer learning-based method for the daily electricity consumption forecasting of large industrial users after business expansion. Methods: Firstly, a dynamic training framework for the prediction model of transfer learning is established, so that the prediction model can dynamically adapt to the capacity change brought about by the expansion of multi-user business. Then, a neural network for predicting daily electricity consumption of industrial users based on multi-resolution time series attention is established, which can deeply mine the characteristics of electricity sequence. Finally, a deep learning model parameter migration and adjustment method considering business expansion is proposed, which can realize efficient migration of prediction models. Results: The effectiveness of the proposed method is demonstrated by comparing it with state-ofthe- art electricity forecasting based on two-year historical data of a specific region. Conclusion: The proposed method is compared with state-of-the-art power forecasting techniques through the validation of local historical data. The obtained results demonstrate the effectiveness of the proposed method.

Understanding the load profiles and electricity consumption patterns of PV mini-grid customers in rural off-grid east africa: A data-driven study

This paper analyzes the load profiles and electricity consumption patterns of different customer types electrified by off-grid solar photovoltaic (PV) mini-grids in two remote towns in Ethiopia using metered data collected over a 20-month period and a survey of 238 customers. Findings show that the load profiles of mini-grid customers differ significantly across locations, sectors, and time. The load curves at site one (Omorate) are interrupted and completely shed off for 13 h every day due to the demand consistently exceeding the generation. By contrast, the mini-grid at site two (Tum) generates enough electricity to meet the demand continuously. The average daily electricity consumption at Omorate, 1065 kW h, is more than 1.5 times the consumption at Tum, 640 kW h; despite the fact that the mini-grid at Omorate has a significantly lower installed capacity than the one at Tum. At both sites, the monthly consumption of productive users is more than three times that of households. At both sites, demand for electricity has significantly increased over time, but at varying rates. Regression analyses showed significant differences in the factors influencing electricity consumption between the two towns. Key policy implications of the study are discussed for informed planning of rural electrification through mini-grids.

Design of Home Automation System with Emergency Control

In today's society, there is a growing demand for smart home solutions that not only enhance convenience but also make a significant impact on affordability and widespread adoption. This paper describes the proposed design of a Node MCU-based Home Automation system with an integrated Wi-Fi module that enables internet-based remote control of home devices. The design utilizes a basic code template to connect with the Tasmota Web interface to configure the ESP module. The system is integrated with Alexa, offering various integration options such as routines, schedules and alarms. Furthermore, it adds a manual control feature for situations where there is no internet connection and an additional level of safety through emergency controls in the event of electronic failure. A comparison of energy usage with and without the use of the proposed system is also reported. A noticeable decrease in daily electricity consumption is observed on using the designed automation system. To ensure cost-effectiveness, locally available components are utilized in the design. Consequently, it ensures that enhanced comfort, convenience, and control over one's living environment are available to a wider audience.

Using machine learning for the optimisation of operations and management in electric systems and networks

This research employs the Random Forest Machine Learning model to predict electricity consumption and detect anomalies in electrical networks. Addressing the energy sector’s challenges, such as supply reliability and renewable energy integration, this model processes historical electricity consumption data, weather conditions, and network events to efficiently forecast demand and identify anomalies. Data cleansing and normalisation preceded the training phase, where the model was fine-tuned using historical data to balance forecast accuracy and overfitting avoidance. The dataset was divided into training (80%) and testing (20%) sets for performance evaluation. Through cross-validation, optimal model hyperparameters were determined. The findings highlight the model’s efficacy in accurately predicting daily electricity consumption in a small, homogenous town. The model achieved a Mean Absolute Error (MAE) of 198.73 MWh and a coefficient of determination (R²) of 0.9387. Temperature, humidity, and wind speed were identified as key influencing factors on consumption levels. Conclusively, the Random Forest model presents a valuable tool for energy management, offering precise consumption forecasting and anomaly detection capabilities. Future work will address computational demands and enhance model integration with other Machine Learning methods for improved performance. This contribution is significant for efficient energy system planning and operation.

Urban‒rural disparities in household energy and electricity consumption under the influence of electricity price reform policies

The gap in electricity consumption between urban and rural households under the influence of electricity price reform policies remain largely unexplored. We construct a mechanistic framework for the impact of the electricity price reform policy on the electricity consumption behaviour of urban and rural residents, and evaluates the carbon reduction effect of the policy guidance through data obtained from a large-scale household energy consumption survey from January 2020 to May 2021. The results of the study show the followings: (1) The daily electricity consumption of rural households is larger and more volatile than that of urban households. However, the growth rate of household electricity consumption of urban residents is larger than that of rural residents. (2) The electricity price reform policy mainly influences urban and rural residents' household electricity consumption behaviour by driving demand motives and comfort motives. (3) Under policy intervention, rural residents’ household energy consumption will decline at a faster rate, and urban residents' total household energy consumption will decline faster before 2025 and then remain in a stable state. To achieve the carbon peak and carbon neutrality goals of the energy system, the low-carbon energy use behaviour of residents can be guided by differentiated policies.

Research on new energy vehicle charging prediction based on Monte Carlo algorithm and its impact on distribution network

With the vigorous promotion of new energy policies, the large-scale charging of new energy vehicles has put forward higher requirements for the safety and stability of the distribution network. Based on the daily driving habits and charging patterns of new energy vehicles, a Monte Carlo sampling algorithm was used to establish a charging load model for new energy vehicles. The model analyzed the driving range, charging load, and time related parameters of new energy vehicles. By analyzing the law of daily charging power of new energy vehicles, the overall trend of charging load of new energy vehicles is obtained. Combined with the daily electricity consumption law of the distribution network, the total load of the distribution network is obtained, and the degree of impact on the distribution network is analyzed. This provides direction for the scheduling of future electric vehicle charging behavior and the construction of related supporting facilities, and provides strong guidance for the optimization and upgrading of the distribution network.

Technical assessment of a stand-alone hybrid renewable system for energy and oxygen optimal production for fishes farming in a residential building using HOMER pro

Electricity holds both economic significance and strategic importance in many regions globally. Persistent challenges related to electricity generation, encompassing aspects like availability, quality, and cost, have impeded the progress of nations for an extended period. While conventional electrochemical storage systems have been the benchmark, they are plagued by issues such as high expenses, technical limitations in power output, and restricted storage capacity. Power generation facilities come with their own array of problems, including noise pollution, environmental impacts, and suboptimal performance, exacerbating the existing concerns of reliability and maintenance. To navigate the path of sustainable global development amidst a sharp upswing in energy consumption, it becomes imperative to foster innovative energy solutions. Independent renewable energy systems (RES) represent a promising avenue for reshaping the prevailing reliance on conventional power grids. The Hybrid Renewable Energy System (HRES) deserves careful calibration, particularly considering the considerable load fluctuations and the need for cost efficiency. The study considers a standalone HRES in a residential area on Manoka Island, where fish farming is a primary activity. This dwelling exhibits a daily electricity consumption averaging at 9.28 kWh, with peak demand hitting 0.88 kW. To evaluate the suitability of the Hybrid Optimization Model for Electric Renewable (HOMER) program, the study investigates the optimal energy generation mix for both sustaining the fish farming industry and fulfilling the energy requirements of the chosen building. The optimization process identifies four key parameters that undergo thorough assessment in this analysis. Delving into the specifics, the study attains an impressive renewable fraction (RF) of 92.5% and achieves a Deficit of Power Supply Probability (DPSP) of 0%. Furthermore, the production of 50 kg of O2 and the emission of 1.69 kg of carbon annually prove adequate for constructing a reliable and environmentally sound energy system. In a broader sense, this research serves as a compelling case study, elucidating the concept of hybrid energy systems within the framework of eco-friendly and sustainable power generation. The study not only underscores the substantial potential of RES for the country but also constitutes a significant stride for researchers active in this field.

Design and dynamic numerical modeling of a hybrid reverse osmosis/adsorption-based distillation system driven by solar dish Stirling engine for enhanced performance and waste heat recovery

In light of green solutions to address the shortage of freshwater shortage, energy crisis, and climate change. Tri-generation renewable systems are recently efficient approaches that can offer multiple energy outputs, such as heat, electricity, and distilled water. This paper presents a new hybrid freshwater production system driven by renewable energy for enhanced performance and waste heat recovery. This hybrid system proposes the utilization of reverse osmosis, an adsorption distillation system, and a solar dish Stirling engine (RO-ADS/SDSE). The cooperative concept is based on the re-desalination of the brine of the RO unit using an adsorption desalination system driven by the heat rejected from the SDSE, while the RO system is driven by the electrical energy produced by the Stirling engine. A mathematical model conducted in MATLAB coupled with EES package is developed to investigate the performance of the proposed RO-ADS/SDSE with and without pressure recovery (PR) mode at different salinity concentrations varying from 30000 to 50000 ppm at a constant feed pressure of 80 bar. The investigation is conducted based on the meteorological data of Tianjin, China. The results showed significant superiority in the performance of the proposed RO-ADS/SDSE system, as the annual freshwater production rate of the RO-ADS/SDSE with PR increased by up to 3000 m3 compared to 1700 m3 for the standalone RO system at a feed salinity of 45000 ppm and feed pressure of 80 bar. On a daily basis, the hourly permeating flow rate of the RO-ADS/SDSE with PR increased from 2.30 m3/h to 2.60 m3/h, and the permeate salinity decreased from 190 to 120 ppm. While the daily electricity consumption decreased from 5.20 to 4.40 kWh/m3. In conclusion, the proposed RO-ADS/SDSE system with PR can be considered a promising approach in maximizing the performance of desalination systems.

Incentivizing residential electricity consumers to increase demand during periods of high local solar generation

Increasing amounts of distributed electricity generation require new measures to avoid costly grid expansions. This paper presents the results of a field trial in which households received price incentives when adjusting their daily electricity consumption to local electricity generation. Effects are estimated with a matched control group to establish causality and further enable the identification of average load shifting effects. The results show that price incentives are effective in shifting households’ electricity demand to peak electricity supply times, while at the same time overall daily electricity demand is not increased.

Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets

Generating electricity from renewable energy instead of fossil fuels brings great benefits to the environment and sustainable development. Thus, assessing the potential of wind and solar energy in agricultural coastal areas can identify sustainable energy solutions for meeting energy demand and producing fresh water for agricultural applications and domestic use. However, it is difficult to accurately assess the wind and solar energy potential in Libya due to the civil war, lack of measured data, and its limited availability. Consequently, this concise work is unique because it is the first to use daily measurement data from Az-Zāwiyah, Libya, for evaluating wind and solar energy based on one year of measured data for 2022. Moreover, the present study aims to investigate the potential of wind and solar energy as promising renewable sources for meeting energy demand in coastal agricultural regions in Libya using multiple datasets for the first time. In this paper, five satellite products (TerraClimate, ERA5, ERA5-Land, MERRA-2, and CFSR) were assessed and compared against measured data for January 2022–December 2022 to understand their suitability, accuracy, and reliability. The results showed that CFSR and ERA5-Land demonstrate the most favorable performance for assessing the wind resource, while all satellite products can be utilized for preliminary solar resource assessment. Then, the assessment of wind and solar resources was evaluated in five agricultural coastal regions (Aljmail, Az-Zāwiyah, Castelverde, Msallatah, and Sabratah) based on the best satellite product for the period of 2000–2022. Furthermore, the performance of the wind and solar power systems was investigated for typical farms, which were chosen to estimate the required energy demand according to daily electrical consumption. The results show that the positive outcomes of implementing these systems were highlighted, with an emphasis on their potential benefits to the entire Libyan agricultural sector. Accordingly, scaling up and generalizing the proposed systems and generalizing them to include all farms in Libya could have a significant impact on national electricity generation, mitigate greenhouse gases, and contribute to the development of the agricultural sector and the country’s economy.

Residential Electricity Consumption Patterns and their Relationship to Commute Times by Mode

In this study, the correlation between emerging electricity consumption patterns in the Chicago area and commute times to work by mode are explored. We found that when aggregating similar daily electricity consumption curves, areas sharing similar electricity patterns are geographically closer to each other and located in regions that are strictly urban or suburban. Furthermore, we modelled these clusters in terms of commute times to work by mode. The findings suggest that morning electricity consumption differs significantly among clusters and that it is caused primarily by long duration public transport trips.

Enerji Tüketiminin Ekonomik Büyüme Üzerindeki Etkileri: Frekans Alanda Nedensellik Analizi

One of the most important indicators of the production channels of developed and developing countries is undoubtedly the energy problem. In addition, the nexus between economic growth and energy use differs from country to country. The main purpose of this article is to examine the nexus between economic growth and energy consumption in Turkey in the period of 2016 and 2022. Daily electricity consumption is used as an indicator of energy consumption, and industrial production index data is used as an indicator of economic growth. Toda-Yamamoto (1995) and Breitung-Candelon (2006) frequency domain tests were used to determine for the causality. According to the outcomes of the Toda-Yamamoto causality test, a unidirectional causality relationship was found from economic growth to energy consumption in the long run. Breitung- Candelon frequency domain causality test results revealed a short (temporary) and long (permanent) relationship from economic growth to energy consumption. In Türkiye, increases in economic growth (expansion of the production channel), increase the energy need more and thus this situation increases import dependency.

Temperature change and daily urban-rural residential electricity consumption in northwestern China: Responsiveness and inequality

Daily temperature plays an important role in the comparison of the electricity consumption of urban and rural residents. Previous studies highlighted the impact of income on the relationship between temperature change and residential electricity consumption; however, urban-rural electricity consumption disparity and temperature-electricity response inequality have been neglected. This paper examines the response of urban and rural residents in a China’s northwestern province to temperature changes in terms of electricity consumption, and uncovers its impact on daily electricity consumption inequality between these two groups by using daily households’ electricity consumption dataset from 2017 to 2019. The study results show: (1) The urban response function exhibits a U-shaped distribution, whereas that of rural residents demonstrates a fluctuating upward trend. (2) When the daily mean temperature falls below 24 °C, the gap between urban and rural electricity consumption increases. (3) During the Spring Festival, the negative effects of extremely low temperatures will be reduced, while at the end of October, they will be increased. These findings can help policymakers reduce the electricity consumption inequality between urban and rural areas in the context of climate change.

Forecasting of short term electric power consumption for different types buildings using improved transfer learning: A case study of primary school in China

Accurate forecasting of electric power consumption is of great significance to the energy saving operation of buildings. However, in practice, few reliable historical data to be employed to forecast the electric power consumption of a building due to the non-availability of a complete system for monitoring of its energy consumption. In order to accurately predict the electric power consumption of a building with a small-size historical data sample, a model is proposed for forecasting daily electric power consumption using TrAdaBoost- Long short term memory (TAB-LSTM). Some new data is collected from the source building with the maximum mean dispersion (MMD), which is combined with the targeted data of the building to generate a training set. The weight of the training set is updated iteratively by transfer learning (TL) and LSTM to predict the daily electric power consumption. This approach is applied to the energy consumption data of a primary school located in the Northwest China. The experimental results show that the proposed TAB-LSTM approach could reduce the average error to 7.8%, and hence it can be used as an efficient tool for intelligent operation and maintenance of existing buildings.

Climate change, air conditioning, and urbanization—evidence from daily household electricity consumption data in China

Climate change, air conditioning, and urbanization—evidence from daily household electricity consumption data in China

Evaluation of the Power Generation Impact for the Mobility of Battery Electric Vehicles

European institutions have decided to ban the sale of Internal Combustion Vehicles (ICEVs) in the EU from 2035. This opens a possible scenario in which, in the not-too-distant future, all vehicles circulating in Europe are likely to be Battery Electric Vehicles (BEVs). The Spanish vehicle fleet is one of the oldest and has the lowest percentage of BEVs in Europe. The aim of this study is to evaluate the hypothetical scenario in which the current mobility of ICEVs is transformed into BEVs, in the geographical area of the province of Barcelona and in Spain in general. The daily electricity consumption, the required installation capacity of wind and solar photovoltaic energies, and the potential reduction of NOx and particulate matter (PM) emissions are estimated. The daily emission reduction would be about 314 tons of NOx and 17 tons of PM in Spain. However, the estimated investment required in Spain to generate the additional electricity from renewable sources would be enormous (over EUR 25.4 billion), representing, for example, 5.5% of the total national budget in 2022.