scholarly journals Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bingjie He ◽  
Jinxiu Xiao ◽  
Qiaorong Dai
Keyword(s):  
Energy Consumption ◽  
Social Welfare ◽  
Electrical Energy ◽  
Data Driven ◽  
Electrical Energy Consumption ◽  
Adjustment Strategy ◽  
Study Results ◽  
The Future ◽  
Generation Capacity ◽  
Energy Provider

The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.

scholarly journals Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

2021 ◽  
Vol 4 (1) ◽  
pp. 90-95
Author(s):  
Sasmitoh Rahmad Riady ◽  
◽  
Tjong Wan Sen ◽  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Consumption ◽  
Multivariate Time Series ◽  
Electrical Energy ◽  
Activation Function ◽  
Time Series Model ◽  
Electrical Energy Consumption ◽  
Process Data ◽  
The Future

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

A persuasive multi-agent simulator to improve electrical energy consumption

2021 ◽  
pp. 1-15
Author(s):  
Fernanda P. Mota ◽  
Cristiano R. Steffens ◽  
Diana F. Adamatti ◽  
Silvia S. Da C Botelho ◽  
Vagner Rosa
Keyword(s):  
Energy Consumption ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Multi Agent

scholarly journals AUDIT ENERGI PADA SEBUAH HOTEL

2012 ◽  
Vol 16 (3) ◽  
pp. 131
Author(s):  
Didik Ariwibowo
Keyword(s):  
Energy Consumption ◽  
Air Conditioning ◽  
Energy Savings ◽  
Electric Energy ◽  
Electrical Energy ◽  
Hot Water ◽  
Electrical Energy Consumption ◽  
Energy Audit ◽  
Electric Energy Consumption ◽  
Pumping System

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

scholarly journals Specific Electrical Energy Consumption and CO2 Emissions Assessment of Agrifood Industries in the Central Region of Portugal

2014 ◽  
Vol 675-677 ◽  
pp. 1880-1886 ◽  
Author(s):  
Pedro D. Silva ◽  
Pedro Dinis Gaspar ◽  
J. Nunes ◽  
L.P.A Andrade
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Food Safety ◽  
Central Region ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Reference Levels ◽  
Energy Characteristics ◽  
Result Analysis

This paper provides a characterization of the electrical energy consumption of agrifood industries located in the central region of Portugal that use refrigeration systems to ensure the food safety. The study is based on the result analysis of survey data and energy characteristics of the participating companies belonging to the following agrifood sectors: meat, dairy, horticultural, distribution and wine. Through the quantification of energy consumption of companies is possible to determine the amount of greenhouse gases (GHGs) emissions indexed to its manufacturing process. Comparing the energy and GHGs emissions indexes of companies of a sector and between sectors is possible to create reference levels. With the results of this work is possible to rating the companies in relation to reference levels of energy and GHGs emissions and thus promote the rational use of energy by the application of practice measures for the improvement of the energy efficiency and the reduction of GHGs emissions.

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