Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time dependencies and proposes a novel energy load forecasting methodology based on sample generation and Sequence-to-Sequence (S2S) deep learning algorithm. The S2S architecture that is commonly used for language translation was adapted for energy load forecasting. Experiments focus on Gated Recurrent Unit (GRU) based S2S models and Long Short-Term Memory (LSTM) based S2S models. All models were trained and tested on one building-level electrical consumption dataset, with five-minute incremental data. Results showed that, on average, the GRU S2S models outperformed LSTM S2S, RNN S2S, and Deep Neural Network models, for short, medium, and long-term forecasting lengths

Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical energy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute parameters of electrical energy consumption. The method considers the timeseries homes of the information and offers parallelization of large-scale facts processing with magnificent operational efficiency, considering the timeseries aspects of the information and the problematic inherent correlations between variables. The exams have been done using the UCI public dataset, and the experimental findings validate the method's efficacy, which has clear, sensible implications for setting up intelligent strength grid dispatching

Forecasting Energy Consumption using Sequence to Sequence Attention models

To combat negative environmental conditions, reduce operating costs, and identify energy savings opportunities, it is essential to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely-used smart meters, have created possibilities for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting, such as FeedForward Neural Networks, are not well-suited for interpreting the time dimensionality of a signal. Consequently, this thesis applies Sequence-to-Sequence (S2S) Recurrent Neural Networks (RNNs) with attention for electrical load forecasting. The S2S and S2S attention architectures commonly used for neural machine translation are adapted for energy forecasting. An RNN enables capturing time dependencies present in the load data, while the S2S RNN model strengthens consecutive sequence prediction by combining two RNNs: encoder and decoder. Adding the attention mechanism to these S2S RNNs alleviates the burden of connecting the encoder and decoder. Presented experiments compare a regular S2S model and four S2S attention models with two baseline models, the conventional Non-S2S RNN and a Deep Neural Network (DNN). Furthermore, each RNN model was evaluated with three different RNN-cells: Vanilla RNN, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) cell. All models were trained and tested on one building-level electrical load dataset, with five-minute incremental data. Results showed that the S2S Bahdanau et al. attention model was the dominant model as it outperformed all other models for nearly all forecasting lengths

Deep Neural Networks As Time Series Forecasters of Energy Demand

Short-term load forecasting is important for the day-to-day operation of natural gas utilities. Traditionally, short-term load forecasting of natural gas is done using linear regression, autoregressive integrated moving average models, and artificial neural networks. Many purchasing and operating decisions are made using these forecasts, and there can be high cost to both natural gas utilities and their customers if the short-term load forecast is inaccurate. Therefore, the GasDay lab continues to explore new ways to make better forecasts. Recently, deep neural networks (DNNs) have emerged as a powerful tool in machine learning problems. DNNs have been shown to greatly outperform traditional methods in many applications, and they have completely revolutionized some fields. Given their success in other machine learning problems, DNNs are evaluated in energy forecasting. This thesis examines many DNN parameters in the context of the short-term load forecasting problem including architecture, input features, and use of synthetic data. The performance of the model is compared against several traditional forecast strategies, including artificial neural networks and linear regression short-term load forecasting strategies. Additionally, the DNN forecaster is evaluated as part of the GasDay ensemble. The DNN forecaster proposed in this thesis offers an average 6.98% improvement in terms of weighted mean absolute percent error (WMAPE) when included as part of the GasDay ensemble. Finally, ideas for future work are discussed

Building Energy Load Forecasting using Deep Neural Networks

Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature

A neural ordinary differential equations based approach for demand forecasting within power grid digital twins

Over the past few years, deep learning (DL) based electricity demand forecasting has received considerable attention amongst mathematicians, engineers and data scientists working within the smart grid domain. To this end, deep learning architectures such as deep neural networks (DNN), deep belief networks (DBN) and recurrent neural networks (RNN) have been successfully applied to forecast the generation and consumption of a wide range of energy vectors. In this work, we show preliminary results for a residential load demand forecasting solution which is realized within the framework of power grid digital twin. To this end, a novel class of deep neural networks is adopted wherein the output of the network is efficiently computed via a black-box ordinary differential equation (ODE) solver. We introduce the readers to the main concepts behind this method followed by a real-world, data driven computational benchmark test case designed to study the numerical effectiveness of the proposed approach. Initial results suggest that the ODE based solutions yield acceptable levels of accuracy for wide range of prediction horizons. We conclude that the method could prove as a valuable tool to develop forecasting models within an electrical digital twin (EDT) framework, where, in addition to accurate prediction models, a time horizon independent, computationally scalable and compact model is often desired.This research that contributed to this paper was funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction (CSIC) and Centre for Digital Built Britain (CDBB) at the University of Cambridge

A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers

Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electricity demand as well as its dependency on various external factors renders STLF a challenging task. To that end, several deep learning models have been proposed in the literature for STLF, reporting promising results. In order to evaluate the accuracy of said models in day-ahead forecasting settings, in this paper we focus on the national net aggregated STLF of Portugal and conduct a comparative study considering a set of indicative, well-established deep autoregressive models, namely multi-layer perceptrons (MLP), long short-term memory networks (LSTM), neural basis expansion coefficient analysis (N-BEATS), temporal convolutional networks (TCN), and temporal fusion transformers (TFT). Moreover, we identify factors that significantly affect the demand and investigate their impact on the accuracy of each model. Our results suggest that N-BEATS consistently outperforms the rest of the examined models. MLP follows, providing further evidence towards the use of feed-forward networks over relatively more sophisticated architectures. Finally, certain calendar and weather features like the hour of the day and the temperature are identified as key accuracy drivers, providing insights regarding the forecasting approach that should be used per case.Comment: Keywords: Short-Term Load Forecasting, Deep Learning, Ensemble, N-BEATS, Temporal Convolution, Forecasting Accurac

A Temporal Neural Network Model for Probabilistic Multi-Period Forecasting of Distributed Energy Resources

Probabilistic forecasts of electrical loads and photovoltaic generation provide a family of methods able to incorporate uncertainty estimations in predictions. This paper aims to extend the literature on these methods by proposing a novel deep-learning model based on a mixture of convolutional neural networks, transformer models and dynamic Bayesian networks. Further, the paper also illustrates how to utilize Stochastic Variational Inference for training output distributions that allow time series sampling, a possibility not given for most state-of-the-art methods which do not use distributions. On top of this, the model also proposes an encoder-decoder topology that uses matrix transposes in order to both train on the sequential and the feature dimension. The performance of the work is illustrated on both load and generation time series obtained from a site representative of distributed energy resources in Norway and compared to state-of-the-art methods such as long-short-term memory. With a single-minute prediction resolution and a single-second computation time for an update with a batch size of 100 and a horizon of 24 hours, the model promises performance capable of real-time application. In summary, this paper provides a novel model that allows generating future scenarios for time series of distributed energy resources in real-time, which can be used to generate profiles for control problems under uncertainty. INDEX TERMS deep learning, generation forecasting, load forecasting, neural networks, probabilistic methods, renewable powerpublishedVersio

Probabilistic Load Forecasting with Deep Conformalized Quantile Regression

The establishment of smart grids and the introduction of distributed generation posed new challenges in energy analytics that can be tackled with machine learning algorithms. The latter, are able to handle a combination of weather and consumption data, grid measurements, and their historical records to compute inference and make predictions. An accurate energy load forecasting is essential to assure reliable grid operation and power provision at peak times when power consumption is high. However, most of the existing load forecasting algorithms provide only point estimates or probabilistic forecasting methods that construct prediction intervals without coverage guarantee. Nevertheless, information about uncertainty and prediction intervals is very useful to grid operators to evaluate the reliability of operations in the power network and to enable a risk-based strategy for configuring the grid over a conservative one. There are two popular statistical methods used to generate prediction intervals in regression tasks: Quantile regression is a non-parametric probabilistic forecasting technique producing prediction intervals adaptive to local variability within the data by estimating quantile functions directly from the data. However, the actual coverage of the prediction intervals obtained via quantile regression is not guaranteed to satisfy the designed coverage level for finite samples. Conformal prediction is an on-top probabilistic forecasting framework producing symmetric prediction intervals, most often with a fixed length, guaranteed to marginally satisfy the designed coverage level for finite samples. This thesis proposes a probabilistic load forecasting method for constructing marginally valid prediction intervals adaptive to local variability and suitable for data characterized by temporal dependencies. The method is applied in conjunction with recurrent neural networks, deep learning architectures for sequential data, which are mostly used to compute point forecasts rather than probabilistic forecasts. Specifically, the use of an ensemble of pinball-loss guided deep neural networks performing quantile regression is used together with conformal prediction to address the individual shortcomings of both techniques