As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

The key challenge for household load forecasting lies in the high volatility and uncertainty of load profiles. Traditional methods tend to avoid such uncertainty by load aggregation (to offset uncertainties), customer classification (to cluster uncertainties) and spectral analysis (to filter out uncertainties). This paper, for the first time, aims to directly learn the uncertainty by applying a new breed of machine learning algorithms—deep learning. However, simply adding layers in neural networks will cap the forecasting performance due to the occurrence of over-fitting. A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers’ load profiles into a pool of inputs. Essentially the model could address the over-fitting issue by increasing data diversity and volume. This paper reports the first attempts to develop a bespoke deep learning application for household load forecasting and achieved preliminary success. The developed method is implemented on Tensorflow deep learning platform and tested on 920 smart metered customers from Ireland. Compared with the state-of-the-art techniques in household load forecasting, the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN by 6.5% in terms of RMSE.

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Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive, and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue To date, substantial works have been conducted on smart meter data analytics To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management We also review the techniques and methodologies adopted or developed to address each application In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security

With the deployment of advanced metering infrastructure (AMI), an avalanche of new energy-use information became available. Better understanding of the actual power consumption patterns of customers is critical for improving load forecasting and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a fresh perspective to the way load forecasting is performed, ranging from very short-term load forecasting to long-term load forecasting at the system level, regional level, feeder level, or even down to the consumer level. This paper addresses the efforts involved in improving the system level intraday load forecasting by applying clustering to identify groups of customers with similar load consumption patterns from smart meters prior to performing load forecasting.

Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

Due to the deregulation of the power system, the electric power industry is undergoing a transformation in terms of its planning and operation strategies. Because of the importance in reducing financial and operational risk, improving load forecasting accuracy is paramount. In some load forecasting applications, K -means clustering is used to group customers prior to forecasting. This method has been shown to improve the accuracy of load predictions. However, there are situations where K -means clustering reduces load forecasting accuracy. This paper studies the factors that affect the performance of K -means clustering. Additionally, several strategies have been proposed to tackle the lower estimation accuracy problems for this clustering algorithm. The data used for validating the proposed strategies associated with the factors is from the Consolidated Edison Company of New York, Inc. (Con Edison). The mean absolute percent error and relative mean square error are utilized to evaluate the forecasting results of K -means-based least squares support vector machines (LS-SVM) and preprocessed K -means-based LS-SVM. Additionally, the outperformance of preprocessed K -means-based LS-SVM is demonstrated via the data results.

Factors that Impact the Accuracy of Clustering-Based Load Forecasting

Better understanding of actual customers' power consumption patterns is critical for improving load forecasting (LF) accuracy and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Though technical literature presented extensive methodologies and models to improve LF accuracy, most of them are based upon aggregated power consumption data at the system level with little or even no information regarding power consumption of different customers' classes. With the deployment of Advanced Metering Infrastructure (AMI), new energy-use information becomes available. AMI data introduces a fresh perspective to perform LF, ranging from very-short- to long- term LF at the system level, or down to the consumer level. However, one critical step to realize these benefits is to develop data management and analysis process to transform AMI data into useful information. This paper addresses the efforts involved in preparing residential customers AMI data as inputs for LF, and introduces the idea of how the preprocessed data could be further enhanced by identifying customers' consumption patterns through the application of clustering. Grouping load profiles based on consumption behavior similarities will reduce the variability of load which is going to be predicted, and therefore, reducing the forecasting error.

An overview of AMI data preprocessing to enhance the performance of load forecasting

Substantial changes in the nature of the loads have occurred recently with the development of the new technologies. This makes it clear that it is necessary to update the load model and provide an accurate representation of the new generation loads. This paper describes the development of the new appliances load model, such as LCD TV, LED TV, and game consoles (Xbox 360™, Wii™, and PS3™), because they become the common appliances that can be found in most of the customers' home. Since ZIP load model is a well known model which establishes the dependency of the power with respect to voltage with physical meaning, this paper follows the same concept to develop the load models that can be seamlessly adopted into commercially available program for better representation.

Load model development for next generation appliances

Due to the deregulation of the power system, the electric power industry is undergoing a transformation in terms of its planning and operation strategies. Because of the importance in reducing financial and operational risk, improving load forecasting accuracy is paramount. In some load forecasting applications, K-means clustering is used to group customers prior to forecasting. This method has been shown to improve the accuracy of load predictions. However, there are situations where K-means clustering reduces load forecasting accuracy. This paper studies the factors that affect the performance of K-means clustering. Additionally, several strategies have been proposed to tackle the lower estimation accuracy problems for this clustering algorithm. The data used for validating the proposed strategies associated with the factors is from Consolidated Edison Company of New York, Inc. (ConEdison). The mean absolute percent error (MAPE) and relative mean square error (RMSE) are utilized to evaluate the forecasting results of K-means based least squares support vector machines (LS-SVM) and preprocessed K-means based LS-SVM. Additionally, the outperformance of preprocessed K-means based LS-SVM is demonstrated via the data results.

Robert Louis Szabados

Papers

Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

Factors that Impact the Accuracy of Clustering-Based Load Forecasting

An overview of AMI data preprocessing to enhance the performance of load forecasting

Load model development for next generation appliances

Factors that impact the accuracy of clustering based load forecasting