Neural network model ensembles for building-level electricity load forecasts

Jetcheva, Jorjeta G.; Majidpour, Mostafa; Chen, Wei-Peng

doi:10.1016/j.enbuild.2014.08.004

Cited by 158 publications

(54 citation statements)

References 10 publications

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“…The performance of an ensemble of ANN was compared with a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, a Seasonal Autoregressive Moving Average (SARMA), a Random Forest, a Double Exponential Smoothing and Multiple Regression in [130], providing the best results. The ANNs composing of the ensemble were trained with different subsets provided by a previous clustering.…”

Section: Ensemble Modelsmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

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Section: Ensemble Modelsmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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“…Our choice of models for comparison is based on the wide use of these classification techniques, SVM and MLP, and wide acceptance as two well-known approaches for classification and prediction by the research community [53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

2018

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Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers' energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

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“…to provide accurate load forecasts [34,35,37]. However, neural networks require significant amount of training data to produce such accurate results [34,36] and hence are not always suited for building load prediction, particularly in newly constructed buildings, where there is not much historical data available. Other algorithms, such as Support Vector Regression [18,19,38,39,43,45], Random Forests [41,42] and Autoregressive models [44,46] have also been used to develop models for building load prediction.…”

Section: Related Workmentioning

confidence: 99%

“…These models eliminate the need for extensive prior knowledge about the buildings and users; however, they often require a large amount of training data for each building of interest [34][35][36]39] or result in insufficient prediction accuracy, especially for long-term forecasting (1-5 day ahead forecasting) [37,40,43,44]. Among many, neural networks have been widely used in load forecasting [18,19,29,34,36,37] to obtain accurate predictions of building loads. Neural Network models take different inputs such as environmental parameters, occupancy information, inputs from the sensors on the HVAC system etc.…”

Section: Related Workmentioning

confidence: 99%

“…and with different input features-temperature, occupancy, weather parameters etc. [34][35][36][37][38][39][40][41][42]. These models eliminate the need for extensive prior knowledge about the buildings and users; however, they often require a large amount of training data for each building of interest [34][35][36]39] or result in insufficient prediction accuracy, especially for long-term forecasting (1-5 day ahead forecasting) [37,40,43,44].…”

Section: Related Workmentioning

confidence: 99%

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Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Prakash

Rajagopal

et al. 2018

Energies

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Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short-and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).

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Neural network model ensembles for building-level electricity load forecasts

Cited by 158 publications

References 10 publications

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

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