Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

https://www.mdpi.com/1996-1073/10/10/1525/pdf

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

This paper presents ensemble models for forecasting big data time series. An ensemble composed of three methods (decision tree, gradient boosted trees and random forest) is proposed due to the good results these methods have achieved in previous big data applications. The weights of the ensemble are computed by a weighted least square method. Two strategies related to the weight update are considered, leading to a static or dynamic ensemble model. The predictions for each ensemble member are obtained by dividing the forecasting problem into h forecasting sub-problems, one for each value of the prediction horizon. These sub-problems have been solved using machine learning algorithms from the big data engine Apache Spark, ensuring the scalability of our methodology. The performance of the proposed ensemble models is evaluated on Spanish electricity consumption data for 10 years measured with a 10-minute frequency. The results showed that both the dynamic and static ensembles performed well, outperforming the individual ensemble members they combine. The dynamic ensemble was the most accurate model achieving a MRE of 2%, which is a very promising result for the prediction of big time series. Proposed ensembles are also evaluated using solar power from Australia for two years measured with 30-min frequency. The results are successfully compared with Artificial Neural Network, Pattern Sequence-based Forecasting and Deep Learning, improving their results.

Multi-step forecasting for big data time series based on ensemble learning

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. 
We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Forecasting: theory and practice

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.

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

The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

https://www.mdpi.com/1996-1073/11/4/949/pdf

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm is introduced in this work Such an algorithm has been developed for distributed computing under the Apache Spark framework Every phase of the algorithm is explained in this work, along with how the optimal values of the input parameters required for the algorithm are obtained In order to test the developed algorithm, a Spanish energy consumption big data time series has been used The accuracy of the prediction has been assessed showing remarkable results Additionally, the optimal configuration of a Spark cluster has been discussed Finally, a scalability analysis of the algorithm has been conducted leading to the conclusion that the proposed algorithm is highly suitable for big data environments

Big data time series forecasting based on nearest neighbours distributed computing with Spark

MV-kWNN: A novel multivariate and multi-output weighted nearest neighbours algorithm for big data time series forecasting

A forecasting algorithm for big data time series is presented in this work. A nearest neighbours-based strategy is adopted as the main core of the algorithm. A detailed explanation on how to adapt and implement the algorithm to handle big data is provided. Although some parts remain iterative, and consequently requires an enhanced implementation, execution times are considered as satisfactory. The performance of the proposed approach has been tested on real-world data related to electricity consumption from a public Spanish university, by using a Spark cluster.

A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting

In recent years the available volume of information has grown considerably due to the development of new technologies such as the sensor networks or smart meters, and therefore, new algorithms able to deal with big data are necessary. In this work the distributed version of the k-means algorithm in the Apache Spark framework is proposed in order to find patterns from a big time series. Results corresponding to the electricity consumptions for years 2011, 2012 and 2013 for two buildings from a public university are presented and discussed. Finally, the performance of the proposed methodology in relation to the computational time is compared with that of Weka as benchmarking.

Ricardo L. Talavera-Llames

Papers

Multi-step forecasting for big data time series based on ensemble learning

Big data time series forecasting based on nearest neighbours distributed computing with Spark

MV-kWNN: A novel multivariate and multi-output weighted nearest neighbours algorithm for big data time series forecasting

A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting

Finding Electric Energy Consumption Patterns in Big Time Series Data