Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting; and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50000 time series divided into 12 different forecasting problems. By training more than 38000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient

State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277

Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression

Renewable energy from wind and solar resources can contribute significantly to the decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless integration with the grid poses significant challenges due to their intermittent generation patterns, which is intensified by the existing uncertainties and fluctuations from the demand side. A resolution is increasing energy storage and standby power generation which results in economic losses. Alternatively, enhancing the predictability of wind and solar energy as well as demand enables replacing such expensive hardware with advanced control and optimization systems. The present research contribution establishes consistent sets of data and develops data-driven models through machine-learning techniques. The aim is to quantify the uncertainties in the electricity grid and examine the predictability of their behaviour. The predictive methods that were selected included conventional artificial neural networks (ANN), support vector regression (SVR) and Gaussian process regression (GPR). For each method, a sensitivity analysis was conducted with the aim of tuning its parameters as optimally as possible. The next step was to train and validate each method with various datasets (wind, solar, demand). Finally, a predictability analysis was performed in order to ascertain how the models would respond when the prediction time horizon increases. All models were found capable of predicting wind and solar power, but only the neural networks were successful for the electricity demand. Considering the dynamics of the electricity grid, it was observed that the prediction process for renewable wind and solar power generation, and electricity demand was fast and accurate enough to effectively replace the alternative electricity storage and standby capacity

Application of machine learning and deep learning methods for load prediction in institutional buildings

Worldwide, the building sector consumes a significant amount of energy in different stages such as construction and operation. Depending on the type of energy source used, buildings have a considerable impact on air pollution and greenhouse gas emissions. To reduce the amount of emissions from the building sector and manage energy consumption, many tools and incentives are used around the world. One of the most recent and successful approaches in this regard is the application of machine learning techniques in building engineering. The increasing availability of real-time data measured by sensors and building automation systems enable the owner and energy planner to analyze the collected information and explore the hidden useful knowledge and use it to answer specific questions such as which parts need retrofit, how much energy can be saved and what would be the cost. At the building level, machine learning has different applications, such as pattern extraction and load prediction. Amongst those, load analysis and energy demand prediction are of specific importance for the building energy managers, as it can lead to a more efficient operation schedule of energy systems in the building. The analysis of load profiles can give a good overview of the energy use and user behavior in the building. Detailed load analysis and understanding is an essential step before the predictive analysis. In this study, electrical load data from three transformers installed in EV building, Concordia University and weather data collected from the weather station installed in EV building were used for load analysis and load prediction. EV building includes two main parts, which are Engineering (ENCS) and visual arts departments (VA). The three transformers considered in this study measure heating, ventilation, and air conditioning (HVAC) load from a mechanical room (located in 17th floor of the EV building) in addition to the plug and miscellaneous loads from ENCS and VA departments. In the load analysis part, the representative daily loads of these three transformers of the building are studied. The magnitude and trend of daily loads are extracted and discussed. The average load from 17th floor’s transformer is found to be 1,441 kW during office hours of weekdays in summer, whereas this load during office time in winter is 991 kW. Note that, this load does not include the gas consumption, used for meeting the heating load during the winter. Regarding the plug load from ENCS and VA department, the average load during office hours of weekdays in summer is 512 kW, and 453 kW, respectively. Moreover, the load reduction during the COVID19 pandemic is studied by comparing the two months (April and May) of 2019 and 2020 for all three transformers. There was a significant reduction of 42 % for the load of 17th floor between April 2019 and April 2020 (weekdays), while 24% and 40% load reduction was observed for ENCS and VA transformers, respectively. Based on the results during COVID 19 period, we see that the existence of people in the building affects the load, but a great part of the load is related to the schedule and policy of the building. That is why there is a good potential to save energy just by changing the schedule and plans that systems are running based on. The second part of the work deals with load prediction using regression analysis and long shortterm memory (LSTM) model. The importance of input variables for load prediction is evaluated in the regression section. In linear regression, twenty scenarios are considered. Each scenario is a different combination of input features. It was found from the results that the best scenario is when all calendar and weather data are considered as input attributes. The best scenario in winter has R2=0.29 and MAPE=24.46, while in summer, R2=0.64 and MAPE=10.47. The results are confirmed with correlation analysis. For this case study, adding meteorological data did not improve prediction in winter significantly because in winter, gas is used for heating and the considered data does not reflect it, but in summer, weather variables were of great importance. Also, specific and unusual events in consumption could be detected with polynomial regression. Regarding load forecasting, LSTM is used as a deep learning model, which considers the sequential load data and predicts future load for different time horizons. Regarding the size of the dataset and LSTM parameters, the best performance was obtained for one-year ahead forecasting with R2= 0.75, and MAPE= 10.97. Another result was that the type of load influences the performance of the LSTM model. Considering different load types, the plug and lighting loads from the ENCS and VA departments could be better predicted than the 17th floor HVAC load, since HVAC load is affected by weather variables that are fluctuating and not easy to predict, but plug loads are more related to the schedule of building. The other influencing factor on prediction performance is the choice of train-set and test-set. The lowest R-squared belongs to the model that has the year 2019 as test-set. The results of this project could be useful for building facility managers to adapt and optimize the schedule of the energy systems and give recommendations to the users to improve energy efficiency

Energy load forecasting using a dual-stage attention-based recurrent neural network

Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Scopus2-s2.0-8511828214