scholarly journals Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 780 ◽  
Author(s):  
Zihao Li ◽  
Daniel Friedrich ◽  
Gareth P. Harrison
Keyword(s):  
Feature Selection ◽  
Energy Consumption ◽  
Selection Process ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Data Driven ◽  
Feature Sets ◽  
Demand Prediction ◽  
Heat Demand ◽  
Mixed Use

There is great interest in data-driven modelling for the forecasting of building energy consumption while using machine learning (ML) modelling. However, little research considers classification-based ML models. This paper compares the regression and classification ML models for daily electricity and thermal load modelling in a large, mixed-use, university building. The independent feature variables of the model include outdoor temperature, historical energy consumption data sets, and several types of ‘agent schedules’ that provide proxy information that is based on broad classes of activity undertaken by the building’s inhabitants. The case study compares four different ML models testing three different feature sets with a genetic algorithm (GA) used to optimize the feature sets for those ML models without an embedded feature selection process. The results show that the regression models perform significantly better than classification models for the prediction of electricity demand and slightly better for the prediction of heat demand. The GA feature selection improves the performance of all models and demonstrates that historical heat demand, temperature, and the ‘agent schedules’, which derive from large occupancy fluctuations in the building, are the main factors influencing the heat demand prediction. For electricity demand prediction, feature selection picks almost all ‘agent schedule’ features that are available and the historical electricity demand. Historical heat demand is not picked as a feature for electricity demand prediction by the GA feature selection and vice versa. However, the exclusion of historical heat/electricity demand from the selected features significantly reduces the performance of the demand prediction.

scholarly journals Return of the features

2018 ◽  
Vol 616 ◽  
pp. A97 ◽  
Author(s):  
A. D’Isanto ◽  
S. Cavuoti ◽  
F. Gieseke ◽  
K. L. Polsterer
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Selection Process ◽  
Sloan Digital Sky Survey ◽  
Learning Models ◽  
Classification Problems ◽  
Forward Selection ◽  
Feature Sets ◽  
Physical Context ◽  
Machine Learning Models

Context. The explosion of data in recent years has generated an increasing need for new analysis techniques in order to extract knowledge from massive data-sets. Machine learning has proved particularly useful to perform this task. Fully automatized methods (e.g. deep neural networks) have recently gathered great popularity, even though those methods often lack physical interpretability. In contrast, feature based approaches can provide both well-performing models and understandable causalities with respect to the correlations found between features and physical processes. Aims. Efficient feature selection is an essential tool to boost the performance of machine learning models. In this work, we propose a forward selection method in order to compute, evaluate, and characterize better performing features for regression and classification problems. Given the importance of photometric redshift estimation, we adopt it as our case study. Methods. We synthetically created 4520 features by combining magnitudes, errors, radii, and ellipticities of quasars, taken from the Sloan Digital Sky Survey (SDSS). We apply a forward selection process, a recursive method in which a huge number of feature sets is tested through a k-Nearest-Neighbours algorithm, leading to a tree of feature sets. The branches of the feature tree are then used to perform experiments with the random forest, in order to validate the best set with an alternative model. Results. We demonstrate that the sets of features determined with our approach improve the performances of the regression models significantly when compared to the performance of the classic features from the literature. The found features are unexpected and surprising, being very different from the classic features. Therefore, a method to interpret some of the found features in a physical context is presented. Conclusions. The feature selection methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.

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.

Systematic Feature Selection Process Applied in Short-Term Data-Driven Building Energy Forecasting Models: A Case Study of a Campus Building

2017 ◽  
Author(s):  
Liang Zhang ◽  
Jin Wen ◽  
Yimin Chen
Keyword(s):  
Feature Selection ◽  
Selection Process ◽  
Building Energy ◽  
Data Driven ◽  
Forecasting Model ◽  
Energy Forecasting ◽  
Systematic Feature ◽  
Selection Framework ◽  
Term Data

An accurate building energy forecasting model is a key component for real-time and advanced control of building energy system and building-to-grid integration. With the fast deployment and advancement of building automation systems, data are collected by hundreds and sometimes thousands of sensors every few minutes in buildings, which provide great potential for data-driven building energy forecasting. To develop building energy forecasting models from a large number of potential inputs, feature selection is a critical procedure to ensure model accuracy and computation efficiency. Though the theory of feature selection is well developed in statistics and machine learning fields, it is not well studied in the application of building energy modeling. In this paper, a feature selection framework proposed in an earlier study is examined using a real campus building in Philadelphia. This feature selection framework combines domain knowledge and statistical methods and is developed for short-term data-driven building energy forecasting. In this case study, the feasibilities of using this feature selection framework in developing whole building energy forecasting model and chiller energy forecasting model are studied. Results show that, for both whole building and chiller energy forecasting applications, the model with systematic feature selection process presents better performance (in terms of cross validation error of forecasted output) than other models including that with conventional inputs and that uses only single feature selection technique.

Electricity Demand Forecasting

2010 ◽  
pp. 296-317 ◽  
Author(s):  
Fawwaz Elakrmi ◽  
Nazih Abu Shikhah
Keyword(s):  
Selection Process ◽  
Demand Forecasting ◽  
Electricity Demand ◽  
Forecasting Models ◽  
Electricity Demand Forecasting ◽  
Long Term Forecasting ◽  
And Performance ◽  
Short Term Forecasting ◽  
The Right

Electricity demand forecasting has attracted the attention of many researchers and power company staff. It still does so because with better forecasting, power companies can approach exact plans with no over- or –under planning. This is reflected as being the right investment in terms of time, money, and performance. In essence a good demand forecast means the right investment plan and therefore, satisfied customers. In reality this is the objective of any business; to be able to estimate the demand as close to reality as possible. The number and extent of demand forecasting methodologies and models developed is tremendous, however, there exists no novel technique that can serve all situations. Basically forecasting models can be divided into statistically based and intelligence-based models. A description of forecasting models helps in identifying the characteristics, features, and strengths of each model. The selection of the most suitable forecasting algorithm is not an easy process. The time frame of the forecast, data availability, the accuracy and cost of the forecast, the application and purpose of the forecast are some of the important parameters in the selection process. A case study of two forecasting models used in Jordan is presented. The discussion of the case study shows that load forecasting in Jordan is based on an intelligence-based model for short term forecasting, and on a combination of traditional statistically-based models for long term forecasting.

scholarly journals Optimizing the Feature Selection Process for Better Accuracy in Datasets with a Large Number of Features (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13767-13768
Author(s):  
Xi Chen ◽  
Afsaneh Doryab
Keyword(s):  
Feature Selection ◽  
Experimental Evaluation ◽  
Selection Process ◽  
High Accuracy ◽  
Frequent Pattern ◽  
Selection Methods ◽  
Feature Sets ◽  
Novel Approach ◽  
Data Points ◽  
Small Set

Most feature selection methods only perform well on datasets with relatively small set of features. In the case of large feature sets and small number of data points, almost none of the existing feature selection methods help in achieving high accuracy. This paper proposes a novel approach to optimize the feature selection process through Frequent Pattern Growth algorithm to find sets of features that appear frequently among the top features selected by the main feature selection methods. Our experimental evaluation on two datasets containing a small and very large number of features shows that our approach significantly improves the accuracy results of the dataset with a very large number of features.

scholarly journals Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1794
Author(s):  
Diogo M. F. Izidio ◽  
Paulo S. G. de Mattos Neto ◽  
Luciano Barbosa ◽  
João F. L. de Oliveira ◽  
Manoel Henrique da Nóbrega Marinho ◽  
...  
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Energy Consumption ◽  
Hybrid System ◽  
Smart Grids ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Selection Process ◽  
Linear Component ◽  
Non Linear

The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE).

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