The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role drawing up national development policy. Therefore, this study proposes Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) Bi-directional Long Short-Term Memory (Bi-LSTM) that named EECP-CBL to predict consumption. In framework, two CNNs first module extract important information from...

A stable power supply is very important in the management of infrastructure. One critical tasks accomplishing this to predict consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on prediction scope, building type can also be an factor since same types buildings show similar patterns. university campus consists several types, a laboratory, administrative office, lecture room, dormitory. temporal...

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes power consumption caused by various events such as fire and heat wave for a day from the present time. On other hand, recurrent neural networks (RNNs), including long short-term memory gated unit (GRU) networks, reflect previous point well to predict current point. Due this property, they have been widely used prediction. The GRU model is simple easy...

Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists diverse components such as meters, energy management systems, storage renewable resources. In particular, to make an effective strategy for the system, accurate load forecasting is necessary. Recently, artificial neural network–based models with good performance been proposed. For forecasting, it critical determine hyperparameters...

Load forecasting is one of the critical tasks for enhancing energy efficiency smart grids. Even though recent deep learning-based load models have shown excellent performance, common problems they faced was that their accuracy highly dependent on data quality and quantity available model training. Collecting a sufficient amount high-quality expensive time-consuming. Recently, generative adversarial network (GAN) has its potential as solution to shortage problem by generating virtual based...

Electric energy consumption forecasting is an interesting, challenging, and important issue in management equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., time series of whole building or individual household smart building. In practice, there many profiles each building, which leads time-consuming expensive system resources. Therefore, this study develops robust framework Multiple Energy Consumption...

Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point generated per month, it not easy collect construct models. This lack can be alleviated using transfer learning techniques. this paper, we propose a novel scheme city or district similar...

For efficient and effective energy management, accurate consumption forecasting is required in management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for electric load forecasting; moreover, perfect data are critical the prediction. However, owing to diverse reasons, such as device malfunctions signal transmission errors, missing frequently observed actual data. Previously, many imputation methods compensate values; however, these achieved...

Computationally intelligent energy forecasting methods for appropriate management at the consumer/producer side have a positive impact on preservation of and play constructive role in tackling global climate change. The production consumption are very high worldwide, demanding with real-world implementation potentials management. In this paper, we survey existing load (ILF) systems, highlight their advantages downsides, briefly discuss workflow employed literature. Furthermore, debate...

Biodiversity conservation is important for the protection of ecosystems. One key task sustainable biodiversity to effectively preserve species’ habitats. However, various reasons, many these habitats have been reduced or destroyed in recent decades. To deal with this problem, it necessary identify potential based on habitat suitability analysis and them. Various techniques estimation proposed date, but they had limited success due limitations data models used. In paper, we propose a novel...

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve self-sufficiency. Because photovoltaic (PV) power has the advantage of less noise easier installation than wind power, it is more flexible in selecting a location for installation. A PV system can be operated efficiently by predicting amount global radiation generation. Thus far, most studies addressed day-ahead probabilistic forecasting predict radiation. However, limitations responding quickly sudden...

Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability effectively learn unstable environmental variables and complex interactions. However, NNs are limited practical industrial application the energy sector because optimization of model structure or hyperparameters is a time-consuming task. This paper proposes two-stage NN method for robust PV forecasting. First, dataset divided into training test sets. In set, several...

Smart grid systems, which have gained much attention due to its ability reduce operation and management costs of power consist diverse components including energy storage, renewable energy, combined cooling, heating (CCHP) systems. The CCHP has been investigated by using the thermal generated during generation process. For efficient utilization numerous accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models proposed, they showed...

An energy-management system requires accurate prediction of the electric load for optimal energy management. However, if amount data is insufficient, it challenging to perform an prediction. To address this issue, we propose a novel forecasting scheme using diverse buildings. We first divide consumption into training and test sets. Then, construct multivariate random forest (MRF)-based models according each building except target in set (RF)-based model limited set. In set, compare with that...