Monthly Electrical Energy Research Articles

Prediction study of electric energy production in important power production base, China

Xinjiang is an important power production base in China, and its electric energy production needs not only meet the demand of Xinjiang's electricity consumption, but also make up for the shortage of electricity in at least 19 provinces or cities in China. Therefore, it is of great significance to know ahead of time the electric energy production of Xinjiang in the future. In such terms, accurate electric energy production forecasts are imperative for decision makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. According to the characteristics of the historical data of monthly electricity generation in Xinjiang from January 2001 to August 2020 , the suitable and widely used SARIMA (Seasonal autoregressive integrated moving mean model) method and Holt-winter method were used to construct the monthly electric energy production in Xinjiang for the first time. The results of our analysis showed that the established SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model had higher prediction accuracy than that of the established Holt-Winters' multiplicative model. We predicted the monthly electric energy production from August 2021 to August 2022 by the SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model, and errors are very small compared to the actual values, indicating that our model has a very good prediction performance. Therefore, based on our study, we provided a simple and easy scientific tool for the future power output prediction in Xinjiang. Our research methods and research ideas can also provide scientific reference for the prediction of electric energy production elsewhere.

Study on Prediction of Energy Conservation and Carbon Reduction in Universities Based on Exponential Smoothing

With the continuous development of China’s economy, the phenomenon of energy scarcity has become more and more prominent, for which China has put forward the strategic goal of carbon peak and carbon neutrality (double carbon target). As densely populated areas, the demand for energy is especially tight in universities. In combination with the work of “conservation-oriented colleges” carried out by the Ministry of Education, the accurate monthly electrical and water energy consumption of Kunming University of Science and Technology from 2018–2021 was counted, and the data were plotted into an energy consumption analysis chart to determine its compliance with the prediction range of the smoothing index prediction model. The corresponding smoothing indices were calculated by writing smoothing formulas through Excel, and, finally, the overall energy consumption indexes for 2022 and 2023 were successfully predicted using the exponential smoothing method. The errors between the real and forecasted values of electricity and water consumption in 2021 are reduced to 2.61% and 2.44%. The smoothing index predicts that the baseline discounted electricity energy consumption in 2022 is 5,423,658.235 kgce and in 2023 is 5,758,865.224 kgce; on the other hand, the baseline discounted water energy consumption in 2022 is predicted to be 632,654.321 kgce, while in 2023 it is predicted to be 652,321.238 kgce. The projected values can be used as an early warning line for the energy consumption index, and long-term management approaches and data support for energy conservation and carbon emission reduction can be effectively provided. The mentioned research provides an important reference for the proposal and implementation of efficient management measures, and provides strong theoretical technical support for the implementation of the carbon peak and neutrality in universities.

Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors

Energy-saving strategies are required to address the increasing global CO2 and electrical energy consumption problems. Therefore, the determinant factors of electrical energy consumption consist of socio-demographic changes, occupant behavior, house and appliance characteristics, or so-called techno-socioeconomic factors, which all need to be assessed. Statistics models, such as the artificial neural network (ANN), can investigate the relationship among those factors. However, the previous ANN model only used limited factors and was conducted in the developed countries of subtropical regions with different determinant factors than those in the developing countries of tropical regions. Furthermore, the previous studies did not investigate the various impacts of techno-socioeconomic factors concerning the performance of the ANN model in estimating monthly electrical energy consumption. The current study develops a model with a more-in depth architecture by examining the effect of additional factors such as socio-demographics, house characteristics, occupant behavior, and appliance characteristics that have not been investigated concerning the model performance. Thus, a questionnaire survey was conducted from November 2017 to January 2018 with 214 university students. The best combination factors in explaining the monthly electrical energy consumption were developed from occupant behavior, with 81% of the variance and a mean absolute percentage error (MAPE) of 20.6%, which can be classified as a reasonably accurate model. The current study’s findings could be used as additional information for occupants or for companies who want to install photovoltaic or wind energy systems.

Improving the performance and economic analysis of photovoltaic panel using copper tubular-rectangular ducted heat exchanger

This research empirically and theoretically assessed the performance of a solar photovoltaic (PV) panel in five different cooling configurations under the weather conditions of Sanandaj, Iran in September 2018. The findings indicated that, compared to the simple PV mode, the increased mean electrical efficiencies over the whole experiment were 0.27%, 0.5%, 0.72%, 0.6, and 0.88% for the PV/w-XP, PV/w-XD, PV/w-2XDP, PV/a,and PV/b-2XDP modes, respectively.Further, the highest electrical power for the PV/w-XP, PV/w-XD, PV/w-2XDP, PV/a, and PV/b-2XDP modes increased by 6.8, 12.17, 16.83, 13.17, and 18.92%, respectively compared to the simple PV mode. The monthly electrical output energy the PV/S, PV/w-XP, PV/w-XD, PV/w-2XDP, PV/a, and PV/b-2XDP modes were 28.24 kWh/A, 29.16 kWh/A, 30.34 kWh/A, 31.81 kWh/A, 31.15 kWh/A, and 32.9 kWh/A, respectively. Then, an economic analysis was carried out for the system with two adjustment coefficients. The results showed that although the payback period with an interest rate and an adjustment coefficient of 10% was 2.72 years longer in the PV/b-2XDP-TE than in the PV/S, the annual worth over 20 years was State USD 3.32 (SANA USD 1.07) higher in the PV/b-2XDP-TE mode than in the PV/S mode by considering merely the electrical section. Hence, considering the outlet hot water and air, PV/b-2XDP-TE is more economical to use.

Potential Energy Saving by ACMV Temperature Setting in University Building

Lecturer office building is particular of important buildings in Universiti Tun Hussein Onn Malaysia. Lecturers will spend their time mostly in office if no learning and teaching process in classroom. This study aims to discover potential energy saving by air conditioner temperature control setting of lecturers building at Block C15, C16 and C17 respectively. Several steps have been done to achieve the objective. A study on type of ACMV, building identification, location, the usage trend, air-conditioner quantity, and the published electrical tariff by TNB were done to familiarize with the building of study. Result was found that about 62% of electrical energy was used on Air Conditioning and Mechanical Ventilation System (ACMV). This is the most energy consumption used in lecturers building. From calculation, the monthly electrical energy consumption of lecturer buildings was near to 56.963MWh. With the proposed potential saving measures, which is air condition temperature setting, lecturers building is estimated to reduce the usage of electrical energy for about 10 to 30% per month. More potential saving could be found in future study if there is designated system that studies the energy consumption trend of the building.

Energy Consumption and Potential Saving in MSI Complex

MSI is a mosque located in Universiti Tun Hussein Onn Malaysia and known as Masjid Sultan Ibrahim. The mosques are considered unique compared to other types of buildings as they exhibit an intermittent operation schedule. This study aims to discover potential saving and provide solutions to achieve cost saving of Masjid Sultan Ibrahim of UTHM. Four potential saving methods were proposed later in this study, which include the installation of sensors, replacement of LED lamp, placement of reminder sign, and human control respectively. Result was found that about 64% of electrical energy was used on ACMV system, 35% on lighting system and the rest were general equipment. From calculation, the monthly electrical energy consumption of Masjid Sultan Ibrahim was near to 36MWh. With the proposed potential saving measures, Masjid Sultan Ibrahim is estimated to reduce the usage of electrical energy for about 13% per month. More potential saving could be found in future study if there is designated system that studies the energy consumption trend of the building.

Continuous-time Bayesian calibration of energy models using BIM and energy data

The present study proposes a framework for the continuous Bayesian calibration of whole building energy simulation (BES) models utilizing data from building information models (BIM) and building energy management systems (BEMS). The ability to import data from BIM and BEMS provides the potential to significantly reduce the time and effort needed for the continuous calibration of BES models. First, five gbXML geometric test cases were used to check the BIM to BES model translation. Translation of the test cases indicates good geometric agreement between the native BIM and the gbXML-based BES model. An actual building calibration case study (with BIM and three years of monthly electrical energy consumption data) was then used to evaluate the proposed continuous calibration method. The results suggest that compared to a non-continuous approach, the continuous Bayesian calibration method showed reduced prediction uncertainty and improved prediction accuracy on a test dataset. The paper also presents information and comparison of the coefficient of variance of the root mean square error (CVRMSE) and the normalized mean biased error (NMBE), recommending looking at their distributions when working with probabilistic BES predictions.

Similarity analysis of the patterns of the monthly electric energy demand time series

The similarity analysis of the monthly electric energy demand time series sequence patterns are shown. The similarity-based forecasting models are allowed to be created because a strong relationship between input and output patterns exists. The chi-square test and the correlation tables were calculated for a few definitions of patterns.

True power consumption labeling and mapping of the health system of the Castilla y León region in Spain by clustering techniques

The latest revisions in April 2018 of the 2010/31/UE and 2012/27/UE Directives on Energy Efficiency and Energy Savings respectively, point out the need of the development of smart energy indexes for buildings with the aim to (i) supervise the energy consumption on the building sector -that currently represents up to one third of the total final energy consumption- and (ii) lead the appropriate actions to transform the current buildings stock to nearly Zero Energy Buildings and Positive Energy Buildings. From public managed buildings, the Health System is the first energy consumer with great difference with other government administration sectors, such as Education or General Administration. Moreover, the energy bill has great impact on the sustainability of the public health care system. However, very few real data were available to characterize the energy demand on public buildings, which are usually the most intensive energy consumers, and efficiency indexes were usually obtained from simulation results. Nevertheless, thanks to the deployment of Smart Metering systems in the last years, it is possible to access to the true energy demand profiles of hundreds of these buildings.In this paper, with three years historical monthly electrical energy consumption data from the health system of the region of Castilla y León in Spain -including hospitals, outpatient facilities, clinics and other medical institutions- and the application of data mining techniques, an end-use electrical energy analysis was conducted to cluster the building housing according to the energy consumption into several energy use intensity clusters and, then, an average value and a Reference Building Energy Index for each cluster is proposed. Thus, a true energy labeling of these buildings based on their distance to the Reference Building Energy Index is done and presented in georeferenced maps.

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.

Photovoltaic system assessment for a school building

The installation of photovoltaic panels (PVs) on the roof of residential and commercial buildings is getting widespread as these areas stand normally idle and can be used for another purpose without losing an inhabited space. Considering the solar potential of Turkey, a significant amount of electricity generation is possible using current PV technology. For this reason, a two-story detached school building located in İzmir, Turkey was taken into consideration and monthly as well as annual coverage ratio of an on-grid PV system for its entire energy requirement (including heating, cooling and lighting) was investigated. The PVs were installed on the south face of the school building roof. A heat pump, with a typical coefficient of performance (COP) value of 2.5, was used for supplying required cooling and heating. The heating, cooling and lighting loads were determined on a monthly basis. The average monthly electrical energy generation of the mounted PVs was calculated using a written code in Energy Equation Solver (EES) software. As a result, the monthly as well as yearly electrical energy demand coverage ratio values for the school using the installed PVs were revealed. Since the school building has a large south faced roof, the installation of PVs is very suitable to meet the cumulative electrical energy need of the heat pump and the lighting load. For Case 1, 180 PVs, which supply the entire yearly demand (with a 110% coverage ratio), were taken into consideration, while for Case 2, 265 PVs, which cover 75% of the roof area, were evaluated. The results showed that between November and March, PV electrical energy generation is not sufficient to meet all energy need of the school for both cases. However, significant coverage ratio values were observed for the rest of the year. In a yearly basis, the PV generation exceeded the building demand by 62% for the Case 2. This conclusion points out that the school can meet its yearly electricity need with the considered PV system and can even have an additional financial profit by selling its surplus PV electricity to the grid. Economic and environmental payback time values as well as simple payback time value were also computed for both investigated cases. The results pointed out a simple payback time of 7.9 years for Case 1 and 7.6 years for Case 2. Energy payback time was determined as 5 years for both systems. The greenhouse gas payback time of 2.7 years and 5.9 years was encountered for coal based and natural gas based calculations.

Thermal Load Reduction with Green Building Envelope

Various studies have confirmed that high thermal resistance in roof & wall can reduce the heat transfer load of the buildings. In this study, experimental investigation of the effects of some sustainable features (such as green roof, reflective coated roof & Fly ash brick (FAB) etc.) on the building has been conducted in the Indian context. For the purpose, two scaled building models i.e. Model I (conventional design) & Model II (green design) have been examined with different design configurations. The sensible heat transfer (SHT) load of the building is reduced by 21% - 29% through the use of FAB with cavity wall in place of Fired clay brick (FCB) wall. The load reduction increases to 26% - 44% by using FAB cavity wall with reflective coating on the roof. This reduction further increases to 64% - 68% with FAB cavity wall along with green roof. In this final scenario, the monthly electrical energy saving for air-conditioning is approximately 5.1 kWhe/m2 per unit envelope area, with associated reduction in GHG emissions up to 6.36 kg CO2eq/m2 per unit envelope area, assuming continuous operation of HVAC systems.

A solar power plant for Curtin University Malaysia

The Curtin University, Sarawak Malaysia (Curtin Sarawak) is the first and largest offshore campus of Curtin University in Perth, Western Australia, and the first foreign university to be established in East Malaysia in partnership with the Sarawak State Government. Today's major concern of Curtin is its monthly electrical energy consumption and the electricity bill since its monthly energy consumption exceeds 0.3 Million kWh, and the corresponding electricity bill surpasses RM 95000. Such a situation necessitates Curtin to curtail the heavy energy consumption with immediate effect. Introducing Renewable Energy Source such as PV Solar Systems is a cost-effective and environmental friendly solution to reduce the exponential increase in energy consumption charges of Curtin. Hence, this paper proposes a 90 kW solar power plant for Curtin Sarawak.

Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models

Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE).

An evolutionary‐based adaptive neuro‐fuzzy inference system for intelligent short‐term load forecasting

AbstractThe continuing growth in size and complexity of electric power systems requires the development of applicable load forecasting models to estimate the future electrical energy demands accurately. This paper presents a novel load forecasting approach called genetic‐based adaptive neuro‐fuzzy inference system (GBANFIS) to construct short‐term load forecasting expert systems and controllers. At the first stage, all records of data are searched by a novel genetic algorithm (GA) to find the most suitable feature of inputs to construct the model. Then, determined inputs are fed into the adaptive neuro‐fuzzy inference system to evolve the initial knowledge‐base of the expert system. Finally, the initial knowledge‐base is searched by another robust GA to induce a better cooperation among the rules by rule weight derivation and rule selection mechanisms. We show the superiority and applicability of our approach by applying it to the Iranian monthly electrical energy demand problem and comparing it with the most frequently adopted approaches in this field. Results indicate that GBANFIS outperforms its rival approaches and is a promising tool for dealing with short‐term load forecasting problems.

Forecasting Monthly Electric Energy Consumption Using Feature Extraction

Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly electricity consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on electric energy consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.

Estimating the Gap of the Electrical Energy Demand of Nineveh Governorate Residential Sector Until Year 2010

Electric energy is of vital importance for daily life activity of the modern societies, and its average individual consumption is one of the most important indicators of economic development. Residential sector of Nineveh Province has suffered since 1991 like all Iraqi Provinces from the lack of electricity supply. This was a setback in the way of growth and development. Therefore data collected of the monthly electrical energy sells of Nineveh residential sector for the period from 2004-2007. They were examined and discussed by Macklarn method: which disregards unusual values and be replaced with the average value of similar months in the data sample. As a hypothesis in this research, there are many variable factors shared by different ratios in the growth of the gap of electrical energy demand that are GDP and temperature. To achieve and test this hypothesis, the descriptive and quantitative approaches have been used in forecasting the gap of electrical energy demand. The goal of this study is to explore the nature of available monthly demanded electricity consumption in residential sector for the period 2004-2007 and forecasting Demand, Supply and its gap from 2008 to 2010. 2008-2010.

Research on Monthly Electric Energy Demand Forecasting under the Influence of Two Calendars

Monthly electric energy demand forecasting plays an important role for the running of power system. China has two tow calendars and they works at the same time. Holidays designed by the lunar calendar affect the regularity of monthly electric load recorded only by the Gregorian one. The normal fuzzy transform is advanced here to quantitatively describe the impact of the Spring Festival and further divided the influence into Jan. and Feb. After excluding the influence, the amended historical data are adopted to training RBF neural network. Experiment results show that because the regularity of raw data is improved, the generalization ability and forecasting precise of RBF neural network are improved.

Dynamic Modelling and Forecasting of Monthly Electric Energy Demand in the Hungarian Electric Energy System

This paper discusses the model building for forecasting Electric Energy Demand /EED/ in the Hungarian Power System. Based on the regularities observed in the monthly EED, some hypothetical model structures are derived, that suggest the use of SARIMA time series models. The effect of temperature can also be treated using transfer function models. By the application of adaptive parameter estimation methods, transfer function model having time varying parameters /due to time dependent temperature sensitivity/ can also be identified.