• Title, Summary, Keyword: forecasting energy consumption

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Assessment of Input Variable Importance and Machine Learning Model Selection for Improving Short Term Load Forecasting on Different Building Types (건물유형별 에너지소비 예측성능 향상을 위한 변수중요도 및 기계학습모델 평가)

  • Jeong, Jin-Hwa;Chae, Young-Tae
    • 한국건축친환경설비학회 논문집
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    • v.11 no.6
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    • pp.586-598
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    • 2017
  • The optimal machine learning model depends on building types was selected by comparing and analyzing short term load forecasting (STLF) performance of primary school and commercial reference building based on 4 machine learning models such as ANN, SVM, CHAID, and, RF. The research consists of data collection-storage, data analysis, meteorological variables extraction, energy consumption forecasting and analysis on typical primary school and commercial building energy model. TMY (Typical Meteorological Year) of Incheon, Korea was applied and based on weather forecasting data provided by the KMA (Korea Meteorological Agency). In case of building energy consumption data, primary school and medium commercial reference building energy consumption data by on EIA's Commercial Buildings Energy Consumption Survey (CBECS) were used. Key weather variables were extracted for each machine learning model between the input variables and the output which is building energy consumption in 15 minutes interval. Finally, forecasting of energy consumption on different building types conducted a comparative analysis of the forecasting performance of building energy consumption based on 4 machine learning models using optimal input variables. The results shows ANN model outperforms other models with 5.44% of CV (RMSE) for 7 days school building energy forecasting trained 8 weeks prior data. Whereas, RF model performs better than the others with 10.96% of CV (RMSE). It may be concluded that the priority of variables which have impacts on energy consumption is important and the most suitable model for energy forecasting is different by the building types.

A Tutorial: Information and Communications-based Intelligent Building Energy Monitoring and Efficient Systems

  • Seo, Si-O;Baek, Seung-Yong;Keum, Doyeop;Ryu, Seungwan;Cho, Choong-Ho
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.7 no.11
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    • pp.2676-2689
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    • 2013
  • Due to increased consumption of energy in the building environment, the building energy management systems (BEMS) solution has been developed to achieve energy saving and efficiency. However, because of the shortage of building energy management specialists and incompatibility among the energy management systems of different vendors, the BEMS solution can only be applied to limited buildings individually. To solve these problems, we propose a building cluster based remote energy monitoring and management (EMM) system and its functionalities and roles of each sub-system to simultaneously manage the energy problems of several buildings. We also introduce a novel energy demand forecasting algorithm by using past energy consumption data. Extensive performance evaluation study shows that the proposed regression based energy demand forecasting model is well fitted to the actual energy consumption model, and it also outperforms the artificial neural network (ANN) based forecasting model.

The Forecasting Power Energy Demand by Applying Time Dependent Sensitivity between Temperature and Power Consumption (시간대별 기온과 전력 사용량의 민감도를 적용한 전력 에너지 수요 예측)

  • Kim, Jinho;Lee, Chang-Yong
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.42 no.1
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    • pp.129-136
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    • 2019
  • In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

Forecasting of Electricity Demand for Fishing Industry Based on Genetic Algorithm approach (유전자 알고리즘에 기반한 수산업 전력 수요 예측에 관한 연구)

  • Kim, Heung-Soe;Lee, Sung-Geun
    • Journal of the Korea Convergence Society
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    • v.8 no.1
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    • pp.19-23
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    • 2017
  • Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

Development of Load Control and Demand Forecasting System

  • Fujika, Yoshichika;Lee, Doo-Yong
    • 제어로봇시스템학회:학술대회논문집
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    • pp.104.1-104
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    • 2001
  • This paper presents a technique to development load control and management system in order to limits a maximum load demand and saves electric energy consumption. The computer programming proper load forecasting algorithm associated with programmable logic control and digital power meter through inform of multidrop network RS 485 over the twisted pair, over all are contained in this system. The digital power meter can measure a load data such as V, I, pf, P, Q, kWh, kVarh, etc., to be collected in statistics data convey to data base system on microcomputer and then analyzed a moving linear regression of load to forecast load demand Eventually, the result by forecasting are used for compost of load management and shedding for demand monitoring, Cycling on/off load control, Timer control, and Direct control. In this case can effectively reduce the electric energy consumption cost for 10% ...

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Development of Peak Power Demand Forecasting Model for Special-Day using ELM (ELM을 이용한 특수일 최대 전력수요 예측 모델 개발)

  • Ji, Pyeong-Shik;Lim, Jae-Yoon
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.64 no.2
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    • pp.74-78
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    • 2015
  • With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

An Index-Based Context-Aware Energy Management System in Ubiquitous Smart Space (유비쿼터스 지능 공간에서의 지수 기반 상황인지 에너지경영 시스템)

  • Kwon, Ohyung;Lee, Yonnim
    • Knowledge Management Research
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    • v.9 no.4
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    • pp.51-63
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    • 2008
  • Effective energy consumption now becomes one of the area of knowledge management which potentially gives global impact. It is considerable for the energy management to optimize the usage of energy, rather than decreasing energy consumption at any cases. To resolve these challenges, an intelligent and personalized system which helps the individuals control their own behaviors in an optimal and timely manner is needed. So far, however, since the legacy energy management systems are nation-wide or organizational, individual-level energy management is nearly impossible. Moreover, most estimating methods of energy consumption are based on forecasting techniques which tend to risky or analysis models which may not be provided in a timely manner. Hence, the purpose of this paper is to propose a novel individual-level energy management system which aims to realize timely and personalized energy management based on context-aware computing approach. To do so, an index model for energy consumption is proposed with a corresponding service framework.

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Analysis of Apartment Power Consumption and Forecast of Power Consumption Based on Deep Learning (공동주택 전력 소비 데이터 분석 및 딥러닝을 사용한 전력 소비 예측)

  • Yoo, Namjo;Lee, Eunae;Chung, Beom Jin;Kim, Dong Sik
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1373-1380
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    • 2019
  • In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

Short-term Load Forecasting of Buildings based on Artificial Neural Network and Clustering Technique

  • Ngo, Minh-Duc;Yun, Sang-Yun;Choi, Joon-Ho;Ahn, Seon-Ju
    • Journal of IKEEE
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    • v.22 no.3
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    • pp.672-679
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    • 2018
  • Recently, microgrid (MG) has been proposed as one of the most critical solutions for various energy problems. For the optimal and economic operation of MGs, it is very important to forecast the load profile. However, it is not easy to predict the load accurately since the load in a MG is small and highly variable. In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings were used. The experimental results show that the proposed approach outperforms the conventional forecasting methods.

A Study on the Comparison of Electricity Forecasting Models: Korea and China

  • Zheng, Xueyan;Kim, Sahm
    • Communications for Statistical Applications and Methods
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    • v.22 no.6
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    • pp.675-683
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    • 2015
  • In the 21st century, we now face the serious problems of the enormous consumption of the energy resources. Depending on the power consumption increases, both China and South Korea face a reduction in available resources. This paper considers the regression models and time-series models to compare the performance of the forecasting accuracy based on Mean Absolute Percentage Error (MAPE) in order to forecast the electricity demand accurately on the short-term period (68 months) data in Northeast China and find the relationship with Korea. Among the models the support vector regression (SVR) model shows superior performance than time-series models for the short-term period data and the time-series models show similar results with the SVR model when we use long-term period data.