Long-Term Electricity Load Forecasting Based On Cascade Forward Backpropagation Neural Network

Nowadays, the Electrical System has an important role in all sectors of life. Electricity has a strategic role. Accuracy and reliability in electricity load forecasting is a great key that can help electricity companies in supplying electricity efficiency, hence, reducing wasted energy. In addition, electricity load forecasting can also help electricity companies to determine the purchase price and power generation. Long-term forecasting is a method of forecasting with a span of more than one year. The historical data will be a reference in solving the problems. This research propose the concept of cascade forward backpropagation for long-term load forecasting. The advantage of this concept is that it can accommodate non-linear conditions without ignoring the linear conditions. This study compared the results of the original data, Feed Forward Backpropagation Neural Network (FFBNN) and Cascade Forward Backpropagation Neural Network (CFBNN). The results were measured by comparing Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE)

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results

Dynamic Probabilistic Model Checking for Sensor Validation in Industry 4.0 Applications

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today’s sensor networks. This paper proposes a novel run-time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor’s trustworthiness enhances the accuracy of the system failure prediction

Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems

Smart buildings are increasingly using Internet of Things (IoT)-based wireless sensing systems to reduce their energy consumption and environmental impact. As a result of their compact size and ability to sense, measure, and compute all electrical properties, Internet of Things devices have become increasingly important in our society. A major contribution of this study is the development of a comprehensive IoT-based framework for smart city energy management, incorporating multiple components of IoT architecture and framework. An IoT framework for intelligent energy management applications that employ intelligent analysis is an essential system component that collects and stores information. Additionally, it serves as a platform for the development of applications by other companies. Furthermore, we have studied intelligent energy management solutions based on intelligent mechanisms. The depletion of energy resources and the increase in energy demand have led to an increase in energy consumption and building maintenance. The data collected is used to monitor, control, and enhance the efficiency of the system

Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms

Abstract Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period

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

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin