Short term power load forecasting using Deep Neural Networks

Accurate load forecasting greatly influences the planning processes undertaken in operation centres of energy providers that relate to the actual electricity generation, distribution, system maintenance as well as electricity pricing. This paper exploits the applicability of and compares the performance of the Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) models on the basis of accuracy and computational performance in the context of time-wise short term forecast of electricity load. The herein proposed method is evaluated over real datasets gathered in a period of 4 years and provides forecasts on the basis of days and weeks ahead. The contribution behind this work lies with the utilisation of a time-frequency (TF) feature selection procedure from the actual “raw” dataset that aids the regression procedure initiated by the aforementioned DNNs. We show that the introduced scheme may adequately learn hidden patterns and accurately determine the short-term load consumption forecast by utilising a range of heterogeneous sources of input that relate not necessarily with the measurement of load itself but also with other parameters such as the effects of weather, time, holidays, lagged electricity load and its distribution over the period. Overall, our generated outcomes reveal that the synergistic use of TF feature analysis with DNNs enables to obtain higher accuracy by capturing dominant factors that affect electricity consumption patterns and can surely contribute significantly in next generation power systems and the recently introduced SmartGrid

Regional And Residential Short Term Electric Demand Forecast Using Deep Learning

For optimal power system operations, electric generation must follow load demand. The generation, transmission, and distribution utilities require load forecasting for planning and operating grid infrastructure efficiently, securely, and economically. This thesis work focuses on short-term load forecast (STLF), that concentrates on the time-interval from few hours to few days. An inaccurate short-term load forecast can result in higher cost of generating and delivering power. Hence, accurate short-term load forecasting is essential. Traditionally, short-term load forecasting of electrical demand is typically performed using linear regression, autoregressive integrated moving average models (ARIMA), and artificial neural networks (ANN). These conventional methods are limited in application for big datasets, and often their accuracy is a matter of concern. Recently, deep neural networks (DNNs) have emerged as a powerful tool for machine-learning problems, and known for real-time data processing, parallel computations, and ability to work with a large dataset with higher accuracy. DNNs have been shown to greatly outperform traditional methods in many disciplines, and they have revolutionized data analytics. Aspired from such a success of DNNs in machine learning problems, this thesis investigated the DNNs potential in electrical load forecasting application. Different DNN Types such as multilayer perception model (MLP) and recurrent neural networks (RNN) such as long short-term memory (LSTM), Gated recurrent Unit (GRU) and simple RNNs for different datasets were evaluated for accuracies. This thesis utilized the following data sets: 1) Iberian electric market dataset; 2) NREL residential home dataset; 3) AMPds smart-meter dataset; 4) UMass Smart Home datasets with varying time intervals or data duration for the validating the applicability of DNNs for short-term load forecasting. The Mean absolute percentage error (MAPE) evaluation indicates DNNs outperform conventional method for multiple datasets. In addition, a DNN based smart scheduling of appliances was also studied. This work evaluates MAPE accuracies of clustering-based forecast over non-clustered forecasts

Deep neural network for load forecasting centred on architecture evolution

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on a large dataset containing hourly load consumption of a residence in London, Ontario shows that the performance of unadjusted weights architecture is comparable to other state-of-the-art approaches. Furthermore, when the architecture weights are adjusted the model accuracy surpassed the state-of-the-art method called LSTM one shot by 3.0%

Differential Evolution Algorithm Based Hyperparameter Selection of Gated Recurrent Unit for Electrical Load Forecasting

Accurate load forecasting remains a formidable challenge in numerous sectors, given the intricate dynamics of dynamic power systems, which often defy conventional statistical models. As a response, time-series methodologies like ARIMA and sophisticated deep learning techniques such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks have demonstrated their mettle by achieving enhanced predictive performance. In our investigation, we delve into the efficacy of the relatively recent Gated Recurrent Network (GRU) model within the context of load forecasting. GRU models are garnering attention due to their inherent capacity to adeptly capture and model temporal dependencies within data streams. Our methodology entails harnessing the power of Differential Evolution, a versatile optimization technique renowned for its prowess in delivering scalable, robust, and globally optimal solutions, especially in scenarios involving non-differentiable, multi-objective, or constrained optimization challenges. Through rigorous analysis, we undertake a comparative assessment of the proposed Gated Recurrent Network model, collaboratively fused with various metaheuristic algorithms, evaluating their performance by leveraging established numerical benchmarks such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our empirical investigations are conducted using power load data originating from the Ontario province, Canada. Our research findings cast a spotlight on the remarkable potential of metaheuristic-augmented Gated Recurrent Network models in substantially augmenting load forecasting precision, offering tailored, optimal hyperparameter configurations uniquely suited to each model's performance characteristics.Comment: 3 figures, 2 tables, Presented @ 3rd Annual BRIC Symposium, 2023 @McMaster University, Hamilton, Canada. arXiv admin note: substantial text overlap with arXiv:2307.1529