scholarly journals Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2357
Author(s):  
Rick Cox ◽  
Shalika Walker ◽  
Joep van der Velden ◽  
Phuong Nguyen ◽  
Wim Zeiler
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Prediction Models ◽  
Storage System ◽  
Demand Forecasting ◽  
Building Energy ◽  
Electricity Demand ◽  
Electricity Load ◽  
Load Shape ◽  
Demand Profile

The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape objective can be met more than 95% of the time for the evaluation period without compromising the thermal comfort of users. Accurate energy demand forecasting is shown to be pivotal for meeting load shape objectives.

scholarly journals Modeling and Optimization of Smart Building Energy Management System Considering Both Electrical and Thermal Load

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 574
Author(s):  
Muhammad Hilal Khan ◽  
Azzam Ul Asar ◽  
Nasim Ullah ◽  
Fahad R. Albogamy ◽  
Muhammad Kashif Rafique
Keyword(s):  
Energy Management ◽  
Management System ◽  
Energy Demand ◽  
Storage System ◽  
Building Energy ◽  
Energy Management System ◽  
Smart Building ◽  
Building Energy Management ◽  
Real Coded Genetic Algorithm ◽  
The Impact

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

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.

scholarly journals The Canadian Surface Prediction Archive (CaSPAr): A Platform to Enhance Environmental Modeling in Canada and Globally

2020 ◽  
Vol 101 (3) ◽  
pp. E341-E356 ◽  
Author(s):  
Juliane Mai ◽  
Kurt C. Kornelsen ◽  
Bryan A. Tolson ◽  
Vincent Fortin ◽  
Nicolas Gasset ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Energy Demand ◽  
Processing Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Demand Forecasting ◽  
Environmental Modeling ◽  
Extreme Rainfall Events ◽  
Numerical Weather ◽  
Surface Prediction

Abstract The Canadian Surface Prediction Archive (CaSPAr) is an archive of numerical weather predictions issued by Environment and Climate Change Canada. Among the products archived on a daily basis are five operational numerical weather forecasts, three operational analyses, and one reanalysis product. The products have hourly to daily temporal resolution and 2.5–50-km spatial resolution. To date the archive contains 394 TB of data while 368 GB of new data are added every night. The data are archived in CF-1.6-compliant netCDF-4 format. The archive is available online (https://caspar-data.ca) since June 2017 and allows users to precisely request data according to their needs, that is, spatial cropping based on a standard shape or uploaded shapefile of the domain of interest and selection of forecast horizons, variables, and issue dates. The degree of customization in CaSPAr is a unique feature relative to other publicly accessible numerical weather prediction archives and it minimizes user download requirements and local processing time. We benchmark the processing time and required storage of such requests based on 216 test scenarios. We also demonstrate how CaSPAr data can be employed to analyze extreme rainfall events. CaSPAr provides access to data that are fundamental for evaluating numerical weather prediction models and demonstrating the improvement in products such as flood and energy demand forecasting systems.

scholarly journals Sustainable Energy Management of Institutional Buildings through Load Prediction Models: Review and Case Study

2021 ◽  
Author(s):  
Antonio Santos Sánchez ◽  
Maria João Regufe ◽  
Ana Mafalda Ribeiro ◽  
Idelfonso B.R. Nogueira
Keyword(s):  
Energy Consumption ◽  
Smart Grids ◽  
Energy Demand ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Average Error ◽  
Load Prediction ◽  
Energy Needs ◽  
Solar Resource

Institutional buildings need smart techniques to predict the energy consumption in a smart grids’ framework. Here, the importance of dynamic load forecasting as a tool to support the decision in smart grids is addressed. In addition, it is reviewed the energy consumption patterns of institutional buildings and the state-of-the-art of load forecast modeling using artificial neural networks. The discussion is supported by historical data from energy consumption in a university building. These data are used to develop a reliable model for the prediction of the electric load in a campus. A neural network model was developed, which can forecast the load with an average error of 6.5%, and this model can also be used as a decision tool to assess the convenience of supplying this load with a set of renewable energy sources. Statistical data that measure the availability of the local renewable sources can be compared with a load model in order to assess how well these energy sources match the energy needs of buildings. This novel application of load models was applied to the campus where a good correlation (Pearson coefficient of 0.803) was found between energy demand and the availability of the solar resource in the campus.

Predicting Peak Energy Demand for an Office Building Using Artificial Intelligence (AI) Approaches

2021 ◽  
Author(s):  
Yuxuan Chen ◽  
Patrick Phelan
Keyword(s):  
Artificial Intelligence ◽  
Energy Demand ◽  
Prediction Models ◽  
Meteorological Data ◽  
Building Energy ◽  
Energy Performance ◽  
Training Data ◽  
Office Building ◽  
Support Vector ◽  
Peak Energy

Abstract Due to the technological advancement in smart buildings and the smart grid, there is increasing desire of managing energy demand in buildings to achieve energy efficiency. In this context, building energy prediction has become an essential approach for measuring building energy performance, assessing energy system efficiency, and developing energy management strategies. In this study, two artificial intelligence techniques (i.e., ANN = artificial neural networks and SVR = support vector regression) are examined and used to predict the peak energy demand to estimate the energy usage for an office building on a university campus based on meteorological and historical energy data. Two-year energy and meteorological data are used, with one year for training and the following year for testing. To investigate the seasonal load trend and the prediction capabilities of the two approaches, two experiments are conducted relying on different scales of training data. In total, 10 prediction models are built, with 8 models implemented on seasonal training datasets and 2 models employed using year-round training data. It is observed that a backpropagation neural network (BPNN) performs better than SVR when dealing with more data, leading to stable generalization and low prediction error. When dealing with less data, it is found that there is no dominance of one approach over another.

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

2015 ◽  
Vol 4 (4) ◽  
pp. 29-45 ◽  
Author(s):  
Karol Fabisz ◽  
Agata Filipowska ◽  
Tymoteusz Hossa
Keyword(s):  
Energy Consumption ◽  
Smart Grids ◽  
Energy Demand ◽  
Additional Data ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Demand Estimation ◽  
Ex Post ◽  
Or Groups ◽  
Energy Demand Forecasting

Nowadays, a lot of attention regarding smart grids' development is devoted to delivery of methods for estimation of the energy demand taking into account the behavior of network participants (being single prosumers or groups of prosumers). These methods take an advantage from an analysis of the ex-post data on energy consumption, usually with no additional data about profiles of prosumers. The goal of this paper is to present and validate a method for an energy demand forecasting based on profiling of prosumers that enables estimation of the energy demand for every user stereotype, every hour, every day of the year and even for every device. The paper presents possible scenarios on how the proposed approach can be used for the benefit of the microgrid.

scholarly journals Interval Grey Prediction Models with Forecast Combination for Energy Demand Forecasting

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 960
Author(s):  
Peng Jiang ◽  
Yi-Chung Hu ◽  
Wenbao Wang ◽  
Hang Jiang ◽  
Geng Wu
Keyword(s):  
Regression Analysis ◽  
Energy Demand ◽  
Prediction Models ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Series Data ◽  
Grey Prediction ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Energy Demand Forecasting

Time series data for decision problems such as energy demand forecasting are often derived from uncertain assessments, and do not meet any statistical assumptions. The interval grey number becomes an appropriate representation for an uncertain and imprecise observation. In order to obtain nonlinear interval grey numbers with better forecasting accuracy, this study proposes a combined model by fusing interval grey numbers estimated by neural networks (NNs) and the grey prediction models. The proposed model first uses interval regression analysis using NNs to estimate interval grey numbers for a real valued sequence; and then a grey residual modification model is constructed using the upper and lower wrapping sequences obtained by NNs. It turns out that two different kinds of interval grey numbers can be estimated by nonlinear interval regression analysis. Forecasting accuracy on real data sequences was then examined by the best non-fuzzy performance values of the combined model. The proposed combined model performed well compared with the other interval grey prediction models considered.

scholarly journals Electricity load forecasting: a systematic review

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Isaac Kofi Nti ◽  
Moses Teimeh ◽  
Owusu Nyarko-Boateng ◽  
Adebayo Felix Adekoya
Keyword(s):  
Energy Consumption ◽  
Demand Forecasting ◽  
Research Work ◽  
Load Forecasting ◽  
Electrical Energy ◽  
Electricity Demand ◽  
Electricity Demand Forecasting ◽  
Global Demand ◽  
Electricity Load ◽  
Electricity Load Forecasting

Abstract The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and commercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand. As a solution to this, numerous studies aimed at estimating future electrical energy demand for residential and commercial purposes to enable electricity generators, distributors, and suppliers to plan effectively ahead and promote energy conservation among the users. Notwithstanding, load forecasting is one of the major problems facing the power industry since the inception of electric power. The current study tried to undertake a systematic and critical review of about seventy-seven (77) relevant previous works reported in academic journals over nine years (2010–2020) in electricity demand forecasting. Specifically, attention was given to the following themes: (i) The forecasting algorithms used and their fitting ability in this field, (ii) the theories and factors affecting electricity consumption and the origin of research work, (iii) the relevant accuracy and error metrics applied in electricity load forecasting, and (iv) the forecasting period. The results revealed that 90% out of the top nine models used in electricity forecasting was artificial intelligence based, with artificial neural network (ANN) representing 28%. In this scope, ANN models were primarily used for short-term electricity forecasting where electrical energy consumption patterns are complicated. Concerning the accuracy metrics used, it was observed that root-mean-square error (RMSE) (38%) was the most used error metric among electricity forecasters, followed by mean absolute percentage error MAPE (35%). The study further revealed that 50% of electricity demand forecasting was based on weather and economic parameters, 8.33% on household lifestyle, 38.33% on historical energy consumption, and 3.33% on stock indices. Finally, we recap the challenges and opportunities for further research in electricity load forecasting locally and globally.

scholarly journals Lesotho electricity demand profile from 2010 to 2030

2021 ◽  
Vol 32 (1) ◽  
pp. 41-57
Author(s):  
M. Mpholo ◽  
M. Mothala ◽  
L. Mohasoa ◽  
D. Eager ◽  
R. Thamae ◽  
...  
Keyword(s):  
Energy Demand ◽  
Developing Economies ◽  
Rural Households ◽  
Household Consumption ◽  
Electricity Demand ◽  
Actual Data ◽  
Percentage Error ◽  
Demand Forecast ◽  
Annual Average ◽  
Demand Profile

This study undertook a 2010 to 2030 electricity demand profile for Lesotho, with 2010 used as the base year. The demand forecast was modelled using the International Atomic Energy Agency Model for Analysis of Energy Demand, largely because of its proven ability to accurately forecast demand in developing economies based on socio-economic, technology and demography variables. The model correlates well with the actual data, where data exists, and predicts that by 2030 Lesotho will achieve a national electrification rate of 54.2%, with 95% for urban households and 14% for rural households, up from 19.4%, 54.1% and 1.8% respectively in the base year. Moreover, in the same period, the forecast for the most likely scenario gives the following results: the maximum demand will increase to 211 MW from 121 MW; the annual average household energy consumption will continue its decline to 1 009 kWh/household from 1 998 kWh/household; and the total consumption will increase to 1 128 284 MWh from 614 868 MWh. The overall low growth rate is attributed to the consistently declining average household consumption that is contrary to international norms. The forecast results gave a root mean square percentage error of 1.5% and mean absolute percentage error of 1.3%, which implied good correlation with the actual data and, hence, confidence in the accuracy of the results. Highlights Between 2030 and 2010: Achievement of national electrification rate of 54.2% up from 19.4%. Electrification: 95% urban, 14% rural households, from 54.1% and 1.8% respectively. The maximum demand will increase to 211 MW from 121 MW. Annual average household consumption will decline to 1 009 kWh/household from 1,998 kWh/household

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