Electric Energy Consumption Data Research Articles

Analysis of Building Electric Energy Consumption Data Using an Improved Cooling Degree Day Method

In cases where a quick insight into the operation of an HVAC system is more important than accuracy, cooling degree days can be used for monitoring electric energy consumption dependent on meteorological conditions. Cooling degree days are calculated from differences between outdoor temperatures above the base temperature and the base temperature itself, therefore containing both climate and building information. The difficulties in applying this method are the determination of base temperature and choosing a procedure for calculating degree days, which vary depending on the resolution of the weather data used. In addition, the cooling degree method has a major flaw, i.e. it considers only a linear dependence between cooling energy consumption and sensible cooling load, thereby ignoring latent loads, which become more significant at higher outdoor temperatures. In this article an analysis of real electric energy consumption data using the cooling degree method and an improved method derived from it that includes latent loads, as well as a comparison between them, are shown. Both methods are applied several times to metered data, each time with a different combination of a method for determining base temperature and a degree day calculation technique.

AUDIT ENERGI PADA SEBUAH HOTEL

Didik Ariwibowo, in this paper explain that energy audit activities conducted through several phases, namely: the initial audit, detailed audit, analysis of energy savings opportunities, and the proposed energy savings. Total energy consumed consists of electrical energy, fuel, and materials in this case is water. Electrical energy consumption data obtained from payment of electricity accounts for a year while consumption of fuel and water obtained from the payment of material procurement. From the calculation data, IKE hotels accounted for 420.867 kWh/m2.tahun, while the IKE standards for the hotel is 300 kWh/m2.tahun. Thus, IKE hotel included categorized wasteful in energy usage. The largest energy consumption on electric energy consumption. Largest electric energy consumption is on the air conditioning (AC-air conditioning) that is equal to 71.3%, and lighting and electrical equipment at 27.28%, and hot water supply system by 4.44%. Electrical energy consumption in AC looks very big. Ministry of Energy and Mineral Resources of the statutes, the profile of energy use by air conditioning at the hotel by 48.5%. With these considerations in the AC target for audit detail as the next phase of activity. The results of a detailed audit analysis to find an air conditioning system energy savings opportunities in pumping systems. Recommendations on these savings is the integration of automation on the pumping system and fan coil units (FCU). The principle of energy conservation in the pumping system is by installing variable speed drives (VSD) pump drive motor to adjust speed according to load on the FCU. Load variations FCU provide input on the VSD pumps to match. Adaptation is predicted pump can save electricity consumption up to 65.7%. Keywords: energy audit, IKE, AC

Analysis and Design of Electric Energy Consumption Inspection Equipment Based on ARM9

The present paper introduces a sort of analysis and design of electric energy consumption inspection equipment based on ARM9, which can inspect multiple electric energy indexes and conduct a real time inspection to electric energy consumption. Both a real time collection and a real time transmission of electric energy consumption data are realized and a real time analysis of these data that are transmitted through the network to the host computer can be carried out as well, the features of which are low power consumption, low cost, very applicable, high real time performance, etc. The paper also describes the system’s basic structure, hardware design, software design and system debugging process.

Incorporation of latent loads into the cooling degree days concept

The cooling degree days concept is a tool to estimate and analyze weather related energy consumption in buildings, i.e. the cooling system electric energy consumption. The main problem with applying this method is that it disregards latent cooling loads. This paper deals with an approach for monitoring electric energy consumption due to cooling in buildings based on cooling degree days, which allows an estimation of latent loads. In addition to applying methods for determining base temperature to base humidity, a new technique is introduced, which is based on a significance test of the enthalpy latent days partial regression coefficient. Analogous to the performance line concept, the influence of latent loads can be presented in the form of a “performance surface” graph. The performance surface is a plot of electric energy consumption as a function of cooling degree days and latent enthalpy days. The above methods are tested on data sets consisting of electric energy consumption data obtained from two buildings and hourly meteorological data.

The Existing Demand for Electricity in Nineveh province and future trends until 2015, The Residential sector as a case study

Electrical energy is of vital importance for the conduct of daily business of contemporary societies, and the average per capita, one of the most important indicators of the level of economic progress. The residential sector in Nineveh province suffering since 1991, a clear lack of processing power, which constitutes an obstacle in the path of growth and development. Therefore, monthly sales of Nineveh residential electric energy consumption data were collected for the period 2004-2009, audit, and addressed by McLaren method. To achieve the goal of research and test the hypothesis descriptive approach was adopted, as well as quantitative approach to forecast the monthly demand of this sector from 2010-2015.

Electric energy consumption in the cotton textile processing stages

Abstract Electric energy is one of the primary energy sources consumpted in cotton textile processing. Current energy cost rate is reported about 8–10% in the total production cost of an ordinary textile product manufactured in Turkey. Significantly important share of this energy cost is electric energy. The aim of this paper was to investigate unit electric energy consumption of cotton textile processing stages using real-time measurements method. Actual and estimated Specific Energy Consumption (SEC) values for electric energy was calculated in the cotton textile processing stages of spinning, warping–sizing, weaving, wet processing and clothing manufacturing. Actual electric energy consumption data are gathered from monthly records of the involved plant managements. Estimated electric energy consumption data is gathered through on-site measurement. Actual and estimated electric energy consumption data and monthly production quantities of the corresponding months are used to facilitate specific electric energy consumption of the plants. It is found that actual electric energy consumption amount per unit textile product is higher than the estimated electric energy consumption amount per unit textile product of each involved textile processing stages.

Neural networks in forecasting electrical energy consumption: univariate and multivariate approaches

This paper presents an artificial neural network (ANN) approach to electric energy consumption (EEC) forecasting in Lebanon. In order to provide the forecasted energy consumption, the ANN interpolates among the EEC and its determinants in a training data set. In this study, four ANN models are presented and implemented on real EEC data. The first model is a univariate model based on past consumption values. The second model is a multivariate model based on EEC time series and a weather-dependent variable, namely, degree days (DD). The third model is also a multivariate model based on EEC and a gross domestic product (GDP) proxy, namely, total imports (TI). Finally, the fourth model combines EEC, DD and TI. Forecasting performance measures such as mean square errors (MSE), mean absolute deviations (MAD), mean percentage square errors (MPSE) and mean absolute percentage errors (MAPE) are presented for all models. Copyright © 2002 John Wiley & Sons, Ltd.