This paper addresses the estimation of household communities' overall energy usage and solar production, considering different prediction horizons. Forecasting electricity demand generation communities can help enrich information available to grid operators better plan their short-term supply. Moreover, households will increasingly need know more about patterns make wiser decisions on appliance...

Traditional electricity price forecasting tends to adopt time-domain methods based on time series, which fail make full use of the regional information market, and ignore extra-territorial factors affecting within region under cross-regional transmission conditions. In order improve accuracy forecasting, this paper proposes a novel spatio-temporal prediction model, is combined with graph convol...

In this study, a novel general multi-step ahead strategy is developed for forecasting time series of air pollutants. The values the predictors at future moments are gathered from official weather forecast sites as independent ex-ante data. They updated with new forecasted every day. Each sample used to build- separate single model that simultaneously predicts pollution levels. sought forecasts ...

Abstract The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network demonstrated a powerful ability to tackle time-series predictions. This study proposed novel hybrid by combining the Xinanjiang (XAJ) and LSTM (XAJ-LSTM) achieve precise multi-step-ahead forecasts. takes forecasts of XAJ as input variables enhance physical mech...

Intelligent time-series forecasting is important in several applied domains. Artificially intelligent methods for forecasting are being consistently sought. The effect of noise on time-series prediction is important to quantify for accurate forecasting with these systems. Conventionally, noise is considered obstructive to accurate forecasting. In this paper we analyse the noise impact on time-s...

The quality of short-term electricity load forecasting is crucial to the operations and trading activities of market participants in an electricity market. In this paper, a multiple equation time series model for intra-day and day-ahead load forecasting is built. The model uses lagged load and temperature as the primary explanatory variables but makes effective use of diurnal characteristics an...