scholarly journals VALIDATION OF A NUMERICAL URBAN WEATHER FORECASTING MODEL COUPLED WITH A BUILDING ENERGY MODEL IN TERMS OF THE REPRODUCIBILITY OF SOLAR IRRADIANCE AND ELECTRICITY DEMAND

2017 ◽  
Vol 73 (2) ◽  
pp. 57-69
Author(s):  
Yukihiro KIKEGAWA ◽  
Youhei YAMAKAWA ◽  
Eriya TOKUTAKE ◽  
Yukitaka OHASHI ◽  
Yuya TAKANE ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Weather Forecasting ◽  
Building Energy ◽  
Electricity Demand ◽  
Energy Model ◽  
Forecasting Model ◽  
Urban Weather

scholarly journals Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee
Keyword(s):  
Predictive Control ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Reference Model ◽  
Weather Prediction ◽  
Building Energy ◽  
Energy Model ◽  
Weather Data ◽  
Rc Building ◽  
Mean Square Errors

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

Building Peak Load Management With High Resolution Weather Data

2015 ◽  
Author(s):  
Yehisson Tibana ◽  
Estatio Gutierrez ◽  
M. Arend ◽  
J. E. Gonzalez
Keyword(s):  
High Resolution ◽  
Weather Forecasting ◽  
Peak Load ◽  
Building Energy ◽  
Energy Model ◽  
Department Of Energy ◽  
Urban Heat Islands ◽  
Weather Data ◽  
Load Management ◽  
Peak Loads

Dense urban environments are exposed to the combined effects of rising global temperatures and urban heat islands. This combination is resulting in increasing trends of energy consumption in cities, associated mostly with air conditioning to maintain indoor human comfort conditions. During periods of extreme summer weather, electrical usage usually reaches peak loads, stressing the electrical grid. The purpose of this study is to explore the use of available, high resolution weather data by effectively preparing a building for peak load management. The subject of study is a 14 floor, 620,782 sq ft building located in uptown Manhattan, New York City (40.819257 N, −73.949288 W). To precisely quantify thermal loads of the buildings for the summer conditions; a single building energy model (SBEM), the US Department of Energy EnergyPlus™ was used. The SBEM was driven by a weather file built from weather data of the urbanized weather forecasting model (uWRF), a high resolution weather model coupled to a building energy model. The SBEM configuration and simulations were calibrated with winter actual gas and electricity data using 2010 as the benchmark year. In order to show the building peak load management, demand response techniques and technologies were implemented. The methods used to prepare the building included generator usage during high peak loads and use of a thermal storage system. An ensemble of cases was analyzed using current practice, use of high resolution weather data, and use of building preparation technologies. Results indicated an average summer peak savings of more than 30% with high resolution weather data.

scholarly journals Multi-step building energy model calibration process based on measured data

2021 ◽  
Vol 252 ◽  
pp. 111380
Author(s):  
José Eduardo Pachano ◽  
Carlos Fernández Bandera
Keyword(s):  
Model Calibration ◽  
Building Energy ◽  
Measured Data ◽  
Energy Model ◽  
Calibration Process

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals Electricity demand forecasting in industrial and residential facilities using ensemble machine learning

2020 ◽  
Author(s):  
Rodrigo Porteiro ◽  
Luis Hernández-Callejo ◽  
Sergio Nesmachnow
Keyword(s):  
Machine Learning ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Residential Facilities ◽  
Forecasting Model ◽  
Model Based ◽  
Electricity Demand Forecasting ◽  
Use Of Resources ◽  
Ensemble Machine Learning ◽  
Ensemble Strategy

This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational intelligence models are developed for day-ahead electricity demand forecasting. An ensemble strategy is applied to build the day-ahead forecasting model based on several one-hour models. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Feature extraction is performed to reduce overfitting, reducing the training time and improving the accuracy. The best model is optimized using grid search strategies on hyperparameter space. Then, an ensemble of 24 instances is generated to build the complete day-ahead forecasting model. Considering the computational complexity of the applied techniques, they are developed and evaluated on the National Supercomputing Center (Cluster-UY), Uruguay. Three different real data sets are used for evaluation: an industrial park in Burgos (Spain), the total electricity demand for Uruguay, and demand from a distribution substation in Montevideo (Uruguay). Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best day ahead model based on ExtraTreesRegressor has a mean absolute percentage error of 2:55% on industrial data, 5:17% on total consumption data and 9:09% on substation data. 

scholarly journals Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8612
Author(s):  
Santanu Kumar Dash ◽  
Michele Roccotelli ◽  
Rasmi Ranjan Khansama ◽  
Maria Pia Fanti ◽  
Agostino Marcello Mangini
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Electricity Demand ◽  
Gradient Boosting ◽  
Forecasting Model ◽  
Demand Forecast ◽  
Daily Consumption ◽  
Energy Provider

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

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