Building power demand forecasting

2016 ◽  
Vol 58 (1) ◽  
Author(s):  
Oleg Valgaev ◽  
Friederich Kupzog
Keyword(s):  
Demand Response ◽  
Renewable Energy Sources ◽  
Demand Forecasting ◽  
Power Demand ◽  
Distribution Grid ◽  
Demand Forecast ◽  
Short Term ◽  
Flexible Loads ◽  
Short Term Forecasting ◽  
Pv Generation

AbstractBuildings acting as flexible loads have been often proposed to mitigate the volatility of renewable energy sources. Thereby, an accurate short-term demand forecast is indispensable for effective demand side management. At the same time, standardized load profiles, commonly used in the distribution grid, are inadequate for load forecasting within building domain. For this PhD, project a novel short-term forecasting model is proposed for that domain. It considers not only residual load, but also scheduled demand response as well as the PV-generation of the building. Moreover, it is not building specific and is, therefore, suitable for area-wide application within building domain.

scholarly journals Very short-term forecasting of power demand of big dynamics objects

2019 ◽  
Vol 84 ◽  
pp. 01007
Author(s):  
Mirosław Parol ◽  
Paweł Piotrowski ◽  
Mariusz Piotrowski
Keyword(s):  
Low Voltage ◽  
Renewable Energy Sources ◽  
Demand Forecasting ◽  
Power Demand ◽  
Short Term ◽  
Forecasting Methods ◽  
Ex Post ◽  
The Subject ◽  
Short Term Forecasting ◽  
Micro Systems

The issue of very short-term forecasting is gaining more and more importance. It covers both the subject of power demand forecasting and forecasting of power generated in renewable energy sources. In particular, for the reason of necessity of ensuring reliable electricity supplies to consumers, it is very important in small energy micro-systems, which are commonly called microgrids. Statistical analysis of data for a sample big dynamics low voltage object will be presented in this paper. The object, in paper author’s opinion, belongs to a class of objects with difficulties in forecasting, in case of very short-term horizon. Moreover, forecasting methods, which can be applied to this type of forecasts, will be shortly characterized. Then results of sample very short-term ex post forecasts of power demand provided by several selected forecasting methods will be presented, as well as some qualitative analysis of obtained forecasts will be carried out. At the end of the paper observations and conclusions concerning analyzed subject, i.e. very short-term forecasting of power demand of big dynamics objects, will be presented.

Ultra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning

2020 ◽  
Vol 35 (4) ◽  
pp. 2937-2948 ◽  
Author(s):  
Mao Tan ◽  
Siping Yuan ◽  
Shuaihu Li ◽  
Yongxin Su ◽  
Hui Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Demand Forecasting ◽  
Power Demand ◽  
Short Term

scholarly journals Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4900 ◽  
Author(s):  
Hongze Li ◽  
Hongyu Liu ◽  
Hongyan Ji ◽  
Shiying Zhang ◽  
Pengfei Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Meteorological Data ◽  
Demand Forecasting ◽  
Dense Layer ◽  
Load Prediction ◽  
Demand Forecast ◽  
Short Term ◽  
Model Framework ◽  
Load Demand

Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM.

Application of deep learning methods for urban water demand forecast modelling

2021 ◽  
Author(s):  
Anjana G Rajakumar ◽  
Avi Anthony ◽  
Vinoth Kumar
Keyword(s):  
Deep Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Demand Forecast ◽  
Short Term ◽  
Learning Methods ◽  
Demand Prediction ◽  
Learning Framework ◽  
Water Demand Forecast

<p>Water demand predictions forms an integral part of sustainable management practices for water supply systems. Demand prediction models aides in water system maintenance, expansions, daily operational planning and in the development of an efficient decision support system based on predictive analytics. In recent years, it has also found wide application in real-time control and operation of water systems as well. However, short term water demand forecasting is a challenging problem owing to the frequent variations present in the urban water demand patterns. There are numerous methods available in literature that deals with water demand forecasting. These methods can be roughly classified into statistical and machine learning methods. The application of deep learning methods for forecasting water demands is an upcoming research area that has found immense traction due to its ability to provide accurate and scalable models. But there are only a few works which compare and review these methods when applied to a water demand dataset. Hence, the main objective of this work is the application of different commonly used deep learning methods for development of a short-term water demand forecast model for a real-world dataset. The algorithms studied in this work are (i) Multi-Layer Perceptron (MLP) (ii) Gated Recurrent Unit (GRU) (iii) Long Short-Term Memory (LSTM) (iv) Convolutional Neural Networks (CNN) and (v) the hybrid algorithm CNN-LSTM. Optimal supervised learning framework required for forecasting the one day ahead water demand for the study area is also identified. The dataset used in this study is from Hillsborough County, Florida, US. The water demand data was available for a duration of 10 months and the data frequency is about once per hour. These algorithms were evaluated based on the (1) Mean Absolute Percentage Error (MAPE) and (ii) Root Mean Squared Error (RMSE) values. Visual comparison of the predicted and true demand plots was also employed to check the prediction accuracy. It was observed that, the RMSE and MAPE values were minimal for the supervised learning framework that used the previous 24-hour data as input. Also, with respect to the forecast accuracy, CNN-LSTM performed better than the other methods for demand forecast, followed by MLP. MAPE values for the developed deep learning models ranged from 5% to 25%. The quantity, frequency and quality of data was also found to have substantial impact on the accuracy of the forecast models developed. In the CNN-LSTM based forecast model, the CNN component was found to effectively extract the inherent characteristics of historical water consumption data such as the trend and seasonality, while the LSTM part was able to reflect on the long-term historical process and future trend. Thus, its water demand prediction accuracy was improved compared to the other methods such as GRU, MLP, CNN and LSTM.</p>

scholarly journals Analysis of Challenges Due to Changes in Net Load Curve in South Korea by Integrating DERs

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1310
Author(s):  
Chi-Yeon Kim ◽  
Chae-Rin Kim ◽  
Dong-Keun Kim ◽  
Soo-Hwan Cho
Keyword(s):  
South Korea ◽  
Power System ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Demand Forecast ◽  
Load Curve ◽  
Target Values ◽  
Load Pattern ◽  
Net Load

The development of Distributed Energy Resources (DERs) is essential in accordance with the mandatory greenhouse gas (GHG) emission reduction policies, resulting in many DERs being integrated into the power system. Currently, South Korea is also focusing on increasing the penetration of renewable energy sources (RES) and EV by 2030 to reduce GHGs. However, indiscriminate DER development can give a negative impact on the operation of existing power systems. The existing power system operation is optimized for the hourly net load pattern, but the integration of DERs changes it. In addition, since ToU (Time-of-Use) tariff and Demand Response (DR) programs are very sensitive to changes in the net load curve, it is essential to predict the hourly net load pattern accurately for the modification of pricing and demand response programs in the future. However, a long-term demand forecast in South Korea provides only the total amount of annual load (TWh) and the expected peak load level (GW) in summer and winter seasons until 2030. In this study, we use the annual photovoltaic (PV) installed capacity, PV generation, and the number of EV based on the target values for 2030 in South Korea to predict the change in hourly net load curve by year and season. In addition, to predict the EV charging load curve based on Monte Carlo simulation, the EV users’ charging method, charging start time, and State-of-Charge (SoC) were considered. Finally, we analyze the change in hourly net load curve due to the integration of PV and EV to determine the amplification of the duck curve and peak load time by year and season, and present the risks caused by indiscriminate DERs development.

A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

2017 ◽  
Vol 142 ◽  
pp. 58-73 ◽  
Author(s):  
Kianoosh G. Boroojeni ◽  
M. Hadi Amini ◽  
Shahab Bahrami ◽  
S.S. Iyengar ◽  
Arif I. Sarwat ◽  
...  
Keyword(s):  
Electric Power ◽  
Time Scale ◽  
Demand Forecasting ◽  
Power Demand ◽  
Medium Term ◽  
Short Term ◽  
Scale Modeling
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