Monthly Electrical Energy Consumption Research Articles

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.

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.

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.

The use of statistical methods to analyze electric-energy consumption by budget institutions

The possibilities of applying statistical methods to analyze the consumption of electric energy by budget organizations and control energy efficiency are studied. Statistical methods are used to consider the influence of the season and climatic conditions on consumption of electric energy, which allowed a simulation model to be created of electric-energy consumption for illumination and other needs according to data of devices considering only the total consumption of electric energy by all groups of consumers. The change in monthly consumption of electric energy within a year is a stochastic value depending on the duration of light in a day and other factors. The statistical characteristics of the monthly consumption of electric energy by individual buildings of Chuvash State University are determined. Dispersion analysis shows that distribution of the values of the monthly electric-energy consumptions by most buildings corresponds to the normal law. The correlation between the monthly electric-energy consumption and duration of light in a day permitted one to carry out a regression analysis and determine the actual average power of the lighting system and actual average power of the rest of the energy consumption of each facility. The results of the statistical analysis make it possible to compile the electric-energy consumption balance, predict electric-energy consumption by the different group of consumers, estimate the efficiency of energy-saving measures, and obtain the data necessary to fill in energy declarations and energy-performance certificates.

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).

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.